diff --git a/_shared_utils/shared_utils/gtfs_analytics_data.yml b/_shared_utils/shared_utils/gtfs_analytics_data.yml
index 712d31dc1..704b4b5c8 100644
--- a/_shared_utils/shared_utils/gtfs_analytics_data.yml
+++ b/_shared_utils/shared_utils/gtfs_analytics_data.yml
@@ -123,6 +123,7 @@ speedmap_segments:
   segments_file: "segment_options/speedmap_segments"
   shape_stop_single_segment: "rollup_singleday/speeds_shape_speedmap_segments"
   shape_stop_single_segment_detail: "rollup_singleday/speeds_shape_speedmap_segments_detail"
+  # excluded_shapes_by_time_of_day: "rollup_singleday/excluded_shapes_by_time_of_day" 
   route_dir_single_segment: "rollup_singleday/speeds_route_dir_speedmap_segments"
   route_dir_multi_segment: "rollup_multiday/speeds_route_dir_speedmap_segments"
   min_trip_minutes: ${speed_vars.time_min_cutoff}
diff --git a/_shared_utils/shared_utils/rt_dates.py b/_shared_utils/shared_utils/rt_dates.py
index b56873dd8..5c3b8dbe3 100644
--- a/_shared_utils/shared_utils/rt_dates.py
+++ b/_shared_utils/shared_utils/rt_dates.py
@@ -74,6 +74,7 @@
     "oct2024g": "2024-10-21",  # additional one-off to capture Amtrak in HQTA
     "nov2024": "2024-11-13",
     "dec2024": "2024-12-11",
+    "jan2025": "2025-01-15"
 }
 
 y2023_dates = [
diff --git a/ca_transit_speed_maps/01_new_speedmaps.ipynb b/ca_transit_speed_maps/01_new_speedmaps.ipynb
index 097b44790..1445e2c51 100644
--- a/ca_transit_speed_maps/01_new_speedmaps.ipynb
+++ b/ca_transit_speed_maps/01_new_speedmaps.ipynb
@@ -34,13 +34,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "23f682c8-187e-4586-a0b2-00dd7569e0c4",
+   "execution_count": 3,
+   "id": "82d7b8ac-3897-4604-9be4-37bd480577e7",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture\n",
-    "speedmap_segs, this_shn = speedmap_utils.read_segments_shn(organization_source_record_id=organization_source_record_id)"
+    "speedmap_segs, this_shn = speedmap_utils.read_segments_shn(organization_source_record_id=organization_source_record_id)\n",
+    "org_shapes = speedmap_utils.read_shapes(speedmap_segs)"
    ]
   },
   {
@@ -70,7 +71,9 @@
   {
    "cell_type": "markdown",
    "id": "d0b9c978-e6a6-4646-a2c0-e3a603f1dfc3",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "# {organization_name}"
    ]
@@ -89,7 +92,8 @@
     "* Route segments are arrow-shaped to indicate direction of travel. Thicker segments indicate more frequent transit routes.\n",
     "* State Highway Network routes are outlined in gray.\n",
     "* Hover over, a segment with your mouse to see the exact speed, route name, and transit service frequency in that segment. \n",
-    "    * Higher-frequency routes (3+ trips per hour) are especially important, since those slowdowns correspond to more vehicles, and riders, delayed in traffic."
+    "    * Higher-frequency routes (3+ trips per hour) are especially important, since those slowdowns correspond to more vehicles, and riders, delayed in traffic.\n",
+    "* Route segments with no data are now shown as thin, dark gray or black segments. These may be due to the route not running at that time of day, or a data availability or processing issue."
    ]
   },
   {
@@ -104,7 +108,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e2aa3d4-acef-4e64-9fa6-07a95ae5112b",
+   "id": "8b335f1d-5d61-4356-8258-68eb0b069989",
    "metadata": {},
    "source": [
     "### 20th Percentile Speeds by Segment"
@@ -113,19 +117,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0a6d0609-3517-46be-a08f-0d04129a62ff",
+   "id": "0003df13-f7d7-4d70-bd8a-91f0cc4e95c3",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture\n",
-    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, analysis_date=ANALYSIS_DATE,\n",
-    "                              time_of_day='AM Peak', map_type='new_speedmap')"
+    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, org_shapes=org_shapes,\n",
+    "                                      analysis_date=ANALYSIS_DATE, time_of_day='AM Peak', map_type='new_speedmap')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22dea4bc-91c3-43b9-a540-d9b52237f51f",
+   "id": "364e6af4-da42-44d6-a252-9b4039e3f590",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +139,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7019c47a-0620-458a-a45a-005f288ee805",
+   "id": "4b7d4db9-6c2c-4151-93d7-c92c512a221d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02bafcad-7e1a-43f7-86ba-9b3c56135a62",
+   "id": "9dbefb68-c2fa-47b9-84ab-9c5e86315f56",
    "metadata": {},
    "source": [
     "### Variation in Speeds by Segment\n",
@@ -156,19 +160,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "82a148cd-1561-4c88-bccf-f552307c2a3d",
+   "id": "f942bbf6-16f4-402b-a7e4-cec9b95e5243",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture\n",
-    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, analysis_date=ANALYSIS_DATE,\n",
-    "                              time_of_day='AM Peak', map_type='new_speed_variation')"
+    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, org_shapes=org_shapes,\n",
+    "                                      analysis_date=ANALYSIS_DATE, time_of_day='AM Peak', map_type='new_speed_variation')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "adf4b569-0029-4295-bc14-70017df6f28e",
+   "id": "4c80b3db-3800-41f0-b0f4-c589ffb2e08e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,7 +182,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d497e0bc-a756-4f05-94d7-174fa45a458c",
+   "id": "187d8255-1332-4b6e-ac75-ed5710830a7b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f60fd2b-66e0-4257-a21c-bdeeece6e8c6",
+   "id": "68ed263d-326c-4f3f-bfb4-ffb963f8f02b",
    "metadata": {
     "tags": []
    },
@@ -197,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fdee73b-a79a-461b-9298-053e4523c41d",
+   "id": "f221d306-9438-4c1f-a59b-ae0b99b5aed5",
    "metadata": {},
    "source": [
     "### 20th Percentile Speeds by Segment"
@@ -206,19 +210,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "876ffe14-cc3f-4257-be00-b06ef8363a16",
+   "id": "12082834-75ee-4715-8e7d-9150d2c8b129",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture\n",
-    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, analysis_date=ANALYSIS_DATE,\n",
-    "                              time_of_day='Midday', map_type='new_speedmap')"
+    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, org_shapes=org_shapes,\n",
+    "                                      analysis_date=ANALYSIS_DATE, time_of_day='Midday', map_type='new_speedmap')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d6858d75-4d44-4ab2-8c44-7f5f91149083",
+   "id": "cb777a85-5704-4d66-badb-08756e9a317c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +232,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a1e92ff3-cbe6-45ca-b063-97807caf21c5",
+   "id": "5171f750-6013-4ca8-bc2f-2bd7678e554f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,7 +241,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "296f23f8-026d-4a98-9ce1-43ec2c048b2e",
+   "id": "816af03a-c7c5-405c-bfbb-1314943d6121",
    "metadata": {},
    "source": [
     "### Variation in Speeds by Segment\n",
@@ -249,19 +253,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31d820ac-0242-47f0-9968-07f4b7122313",
+   "id": "300161cd-a92a-4260-8ccf-84c5797e6ec6",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture\n",
-    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, analysis_date=ANALYSIS_DATE,\n",
-    "                              time_of_day='Midday', map_type='new_speed_variation')"
+    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, org_shapes=org_shapes,\n",
+    "                                      analysis_date=ANALYSIS_DATE, time_of_day='Midday', map_type='new_speed_variation')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ba8f1e35-3503-41d7-a5ad-60606267cdab",
+   "id": "71145071-573a-4c97-9d5d-ac19e43b5a97",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +275,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2633b5c9-3510-4690-8b7b-9abbff45b8e0",
+   "id": "bd852a1e-0cc8-4e6d-acf0-e38b35609deb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f5c3087-8fe4-47fc-bccd-d1eb2f8e092e",
+   "id": "ac317854-05fe-4a96-84b3-664ebffe083d",
    "metadata": {
     "tags": []
    },
@@ -290,7 +294,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15ced50f-7135-4807-a01f-b9768256b27b",
+   "id": "bd10bcf2-e91f-475f-a7bb-ea29ed1b8eb6",
    "metadata": {},
    "source": [
     "### 20th Percentile Speeds by Segment"
@@ -299,19 +303,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b341b050-4fa9-4f25-a276-886b3c62a586",
+   "id": "11cf5818-f909-4d56-b358-b48f29ed256f",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture\n",
-    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, analysis_date=ANALYSIS_DATE,\n",
-    "                              time_of_day='PM Peak', map_type='new_speedmap')"
+    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, org_shapes=org_shapes,\n",
+    "                                      analysis_date=ANALYSIS_DATE, time_of_day='PM Peak', map_type='new_speedmap')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d4cf9996-0239-4cb4-92c5-ed04d57a7b4c",
+   "id": "c86ef14d-0528-4cb4-a347-55607f547159",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,7 +325,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c6ea5f19-d416-4bf5-ab9b-3199af92ed90",
+   "id": "db6634b3-a794-47a4-a4e2-6a875aea1a0c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -330,7 +334,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19324dd0-38e2-44b6-9552-2c67f44ffa4a",
+   "id": "4ebb8a11-b214-4a0e-bd24-f29863d7832b",
    "metadata": {},
    "source": [
     "### Variation in Speeds by Segment\n",
@@ -342,19 +346,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eb50554c-cb63-464d-9d0f-ed722d24f422",
+   "id": "6e6f502e-7a81-4582-8a70-7c20c0f04bdf",
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture\n",
-    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, analysis_date=ANALYSIS_DATE,\n",
-    "                              time_of_day='PM Peak', map_type='new_speed_variation')"
+    "link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, org_shapes=org_shapes,\n",
+    "                                      analysis_date=ANALYSIS_DATE, time_of_day='PM Peak', map_type='new_speed_variation')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b11b79c6-d87d-4563-9cd9-83a5a7294db6",
+   "id": "7ebde3a1-a6ab-4c23-9105-9b163c47fb0f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,7 +368,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "37190c5b-5943-4172-9a91-892bf36c7a2b",
+   "id": "7ed2bb7c-dd0e-4aeb-8ebe-2ff9835f6fbb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -373,7 +377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73f4bc99-f78f-4f37-b050-5f12cbe4ca7a",
+   "id": "749a63f1-35d5-4f5e-bc44-871943868e71",
    "metadata": {
     "tags": []
    },
@@ -386,7 +390,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "40979f68-b80b-4879-8892-3271a74efc8b",
+   "id": "52ef19f8-4078-4cbd-8049-a01963eb4d06",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,7 +399,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5dadca19-df7a-4fc8-8955-210b51f4b1c4",
+   "id": "70846ffd-28d6-4042-80bc-a79d3959a26e",
    "metadata": {},
    "source": [
     "## Additional Time Periods\n",
@@ -410,16 +414,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "05570269-2c18-4d49-b879-b3466e735838",
+   "id": "6bea466f-f2fb-44b4-a26f-44bcf20abe7a",
    "metadata": {},
    "outputs": [],
    "source": [
     "for time_period in [period for period in speedmap_segs.time_of_day.unique() if period not in ['AM Peak', 'Midday', 'PM Peak']]:\n",
-    "    link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, analysis_date=ANALYSIS_DATE,\n",
-    "                              time_of_day=time_period, map_type='new_speedmap')\n",
+    "    link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, org_shapes=org_shapes,\n",
+    "                                          analysis_date=ANALYSIS_DATE, time_of_day=time_period, map_type='new_speedmap')\n",
     "    speedmap_utils.render_spa_link(link, text=f\"{time_period} Speeds\")\n",
-    "    link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, analysis_date=ANALYSIS_DATE,\n",
-    "                              time_of_day=time_period, map_type='new_speed_variation')\n",
+    "    link = speedmap_utils.map_time_period(district_gdf=this_shn, speedmap_segs=speedmap_segs, org_shapes=org_shapes,\n",
+    "                                          analysis_date=ANALYSIS_DATE, time_of_day=time_period, map_type='new_speed_variation')\n",
     "    speedmap_utils.render_spa_link(link, text=f\"{time_period} Speed Variation\")"
    ]
   }
diff --git a/ca_transit_speed_maps/README.md b/ca_transit_speed_maps/README.md
index dbca276c6..20e7626c2 100644
--- a/ca_transit_speed_maps/README.md
+++ b/ca_transit_speed_maps/README.md
@@ -45,6 +45,8 @@ We're aligining with segment speeds published via the [open data portal!](https:
 While the datasets don't fully match just yet, this site is now based on the same underlying data. This should help us update it more often, enhance accuracy for complex route shapes, and include as many transit operators as possible.
 
 The site also includes a more informative speed and variation by time of day chart at the bottom of the page.
+
+Route segments with no data are now shown as thin, dark gray or black segments on the maps. These may be due to the route not running at that time of day, or a data availability or processing issue.
     
 ## Data Sources
 Archived GTFS-Realtime Vehicle Positions data, plus corresponding GTFS Schedule data.
diff --git a/ca_transit_speed_maps/speedmap_utils.py b/ca_transit_speed_maps/speedmap_utils.py
index 37a9dc20e..626e7b934 100644
--- a/ca_transit_speed_maps/speedmap_utils.py
+++ b/ca_transit_speed_maps/speedmap_utils.py
@@ -4,6 +4,7 @@
 import geopandas as gpd
 import update_vars_index
 from shared_utils import rt_utils, catalog_utils
+from segment_speed_utils import helpers
 from calitp_data_analysis.geography_utils import CA_NAD83Albers
 import datetime as dt
 import altair as alt
@@ -24,6 +25,16 @@ def read_segments_shn(organization_source_record_id: str) -> (gpd.GeoDataFrame,
     
     return (speedmap_segs, this_shn)
 
+def read_shapes(speedmap_segs: gpd.GeoDataFrame):
+
+    shapes = helpers.import_scheduled_shapes(update_vars_index.ANALYSIS_DATE, columns=['shape_array_key', 'geometry'])
+    trips = helpers.import_scheduled_trips(update_vars_index.ANALYSIS_DATE, columns=['shape_array_key', 'shape_id', 'route_id',
+                                                                   'route_short_name', 'gtfs_dataset_key']).drop_duplicates()
+    shapes = shapes.merge(trips, on='shape_array_key')
+    org_shapes = shapes.merge(speedmap_segs[['schedule_gtfs_dataset_key']].drop_duplicates(),
+                              on='schedule_gtfs_dataset_key')
+    return org_shapes
+
 def prepare_segment_gdf(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
     '''
     Project segment speeds gdf and add column for rich speedmap display
@@ -67,8 +78,33 @@ def map_shn(district_gdf: gpd.GeoDataFrame):
     spa_map_state = export_result['state_dict']
     return spa_map_state
 
-def map_time_period(district_gdf: gpd.GeoDataFrame, speedmap_segs: gpd.GeoDataFrame, analysis_date: dt.date,
-                    time_of_day: str, map_type: str):
+def map_excluded_shapes(existing_state: dict, speedmap_segs: gpd.GeoDataFrame, shapes_gdf: gpd.GeoDataFrame,
+                       time_of_day: str, analysis_date: dt.date):
+    '''
+    
+    '''
+    display_date = analysis_date.strftime('%B %d %Y (%A)')
+    filename = f"{analysis_date}_{speedmap_segs.organization_source_record_id.iloc[0]}_excluded_shapes_{time_of_day}"
+    title = f"{speedmap_segs.organization_name.iloc[0]} {display_date} Excluded Shapes {time_of_day}"
+
+    shapes_gdf = shapes_gdf[['shape_id', 'route_id', 'route_short_name', 'geometry']]
+    speedmap_segs = speedmap_segs.dissolve()
+    speedmap_segs.geometry = speedmap_segs.buffer(35) #  slightly bigger than parallel_offset in rt_utils
+    excluded_shapes = shapes_gdf.overlay(speedmap_segs, how='difference')
+    excluded_shapes['color'] = [(50,50,50) for _ in excluded_shapes.iterrows()] #  make it dark gray!
+    excluded_shapes['info'] = "No data in time period"
+    excluded_shapes.geometry = excluded_shapes.buffer(8) #  for display
+    
+    if excluded_shapes.empty:
+        return {}
+    export_result = rt_utils.set_state_export(excluded_shapes, subfolder = update_vars_index.GEOJSON_SUBFOLDER, filename = filename,
+                        map_title = title, existing_state = existing_state)
+    
+    return export_result['state_dict']
+
+
+def map_time_period(district_gdf: gpd.GeoDataFrame, speedmap_segs: gpd.GeoDataFrame, org_shapes: gpd.GeoDataFrame,
+                    analysis_date: dt.date, time_of_day: str, map_type: str):
     '''
     Always add State Highway Network first.
     '''
@@ -78,6 +114,9 @@ def map_time_period(district_gdf: gpd.GeoDataFrame, speedmap_segs: gpd.GeoDataFr
         return None
     color_col = {'new_speedmap': 'p20_mph', 'new_speed_variation': 'fast_slow_ratio'}[map_type]
     shn_state = map_shn(district_gdf)
+    excluded_shapes_state = map_excluded_shapes(shn_state, speedmap_segs, org_shapes,
+                                                time_of_day, analysis_date)
+    
     display_date = analysis_date.strftime('%B %d %Y (%A)')
     filename = f"{analysis_date}_{speedmap_segs.organization_source_record_id.iloc[0]}_{map_type}_{time_of_day}"
     title = f"{speedmap_segs.organization_name.iloc[0]} {display_date} {time_of_day}"
@@ -94,7 +133,7 @@ def map_time_period(district_gdf: gpd.GeoDataFrame, speedmap_segs: gpd.GeoDataFr
         map_type=map_type,
         color_col=color_col, cmap=cmap, legend_url=legend_url,
         map_title=title,
-        existing_state = shn_state)
+        existing_state = excluded_shapes_state)
     
     spa_link = export_result['spa_link'] 
     return spa_link
diff --git a/gtfs_funnel/02_debug_st_direction.ipynb b/gtfs_funnel/02_debug_st_direction.ipynb
new file mode 100644
index 000000000..be0e1d39d
--- /dev/null
+++ b/gtfs_funnel/02_debug_st_direction.ipynb
@@ -0,0 +1,1025 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1371ca85-275c-4fb1-a81b-69c7af148053",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from update_vars import analysis_date_list, SCHED_GCS, GTFS_DATA_DICT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "44d84321-f7b5-4d3b-bc64-5854512a2a18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "import geopandas as gpd\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import sys\n",
+    "\n",
+    "from loguru import logger\n",
+    "\n",
+    "from calitp_data_analysis import utils\n",
+    "from shared_utils import rt_utils\n",
+    "from segment_speed_utils import helpers\n",
+    "from segment_speed_utils.project_vars import PROJECT_CRS\n",
+    "from update_vars import GTFS_DATA_DICT, RT_SCHED_GCS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "41fd87b3-1a9d-413b-9dda-8e57e61b3a78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2025-01-15']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "analysis_date_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3a63eedc-f625-4c4f-b48c-ccbdc2019b29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_date = analysis_date_list[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cb9f228-4e60-49c4-a283-c300ea59110f",
+   "metadata": {},
+   "source": [
+    "## Next steps to solve https://github.com/cal-itp/data-analyses/pull/1356"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "228909ff-9402-4be4-b2dc-4ab09ac32f2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import stop_times_with_direction as sd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b8101ac0-cdb9-4bfe-9bd5-04bf9a6b247c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "EXPORT_FILE = GTFS_DATA_DICT.rt_vs_schedule_tables.stop_times_direction\n",
+    "\n",
+    "scheduled_stop_times = sd.prep_scheduled_stop_times(analysis_date)\n",
+    "\n",
+    "trip_cols = [\"trip_instance_key\"]\n",
+    "trip_stop_cols = [\"trip_instance_key\", \"stop_sequence\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "585b67ee-6000-4282-abd1-e117d9549105",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feed_key</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>schedule_gtfs_dataset_key</th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>shape_array_key</th>\n",
+       "      <th>stop_name</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2714578</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>9255</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Chase / Katherine</td>\n",
+       "      <td>POINT (143436.362 -420025.038)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2714814</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>9256</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Chase / Lennox</td>\n",
+       "      <td>POINT (143169.561 -420048.613)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2716121</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>695</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Chase / Ranchito</td>\n",
+       "      <td>POINT (143956.624 -419845.037)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2716157</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>9254</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Chase / Hazeltine</td>\n",
+       "      <td>POINT (143668.058 -419967.744)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2716193</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>696</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Van Nuys / Chase</td>\n",
+       "      <td>POINT (142883.028 -420093.918)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2742567</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>6744</td>\n",
+       "      <td>114</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Valley Circle / Canzonet</td>\n",
+       "      <td>POINT (125364.090 -426918.777)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2742581</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>14487</td>\n",
+       "      <td>86</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Saticoy / Sale</td>\n",
+       "      <td>POINT (127206.779 -422123.347)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2742598</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>15184</td>\n",
+       "      <td>98</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Vanowen / Berquist</td>\n",
+       "      <td>POINT (125847.611 -423758.251)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2742612</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>1346</td>\n",
+       "      <td>79</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Saticoy / De Soto</td>\n",
+       "      <td>POINT (130023.517 -422076.878)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2742629</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>15106</td>\n",
+       "      <td>106</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Valley Circle / Hamlin</td>\n",
+       "      <td>POINT (123902.277 -424437.038)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 feed_key stop_id  stop_sequence  \\\n",
+       "2714578  7a5a0fa93eb3c7a402c0887a4f11f066    9255             37   \n",
+       "2714814  7a5a0fa93eb3c7a402c0887a4f11f066    9256             38   \n",
+       "2716121  7a5a0fa93eb3c7a402c0887a4f11f066     695             35   \n",
+       "2716157  7a5a0fa93eb3c7a402c0887a4f11f066    9254             36   \n",
+       "2716193  7a5a0fa93eb3c7a402c0887a4f11f066     696             39   \n",
+       "2742567  7a5a0fa93eb3c7a402c0887a4f11f066    6744            114   \n",
+       "2742581  7a5a0fa93eb3c7a402c0887a4f11f066   14487             86   \n",
+       "2742598  7a5a0fa93eb3c7a402c0887a4f11f066   15184             98   \n",
+       "2742612  7a5a0fa93eb3c7a402c0887a4f11f066    1346             79   \n",
+       "2742629  7a5a0fa93eb3c7a402c0887a4f11f066   15106            106   \n",
+       "\n",
+       "                schedule_gtfs_dataset_key                 trip_instance_key  \\\n",
+       "2714578  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "2714814  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "2716121  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "2716157  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "2716193  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "2742567  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "2742581  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "2742598  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "2742612  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "2742629  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "\n",
+       "                          shape_array_key                 stop_name  \\\n",
+       "2714578  59459dbb9f7ce418d6f88f65e905d583         Chase / Katherine   \n",
+       "2714814  59459dbb9f7ce418d6f88f65e905d583            Chase / Lennox   \n",
+       "2716121  59459dbb9f7ce418d6f88f65e905d583          Chase / Ranchito   \n",
+       "2716157  59459dbb9f7ce418d6f88f65e905d583         Chase / Hazeltine   \n",
+       "2716193  59459dbb9f7ce418d6f88f65e905d583          Van Nuys / Chase   \n",
+       "2742567  59459dbb9f7ce418d6f88f65e905d583  Valley Circle / Canzonet   \n",
+       "2742581  59459dbb9f7ce418d6f88f65e905d583            Saticoy / Sale   \n",
+       "2742598  59459dbb9f7ce418d6f88f65e905d583        Vanowen / Berquist   \n",
+       "2742612  59459dbb9f7ce418d6f88f65e905d583         Saticoy / De Soto   \n",
+       "2742629  59459dbb9f7ce418d6f88f65e905d583    Valley Circle / Hamlin   \n",
+       "\n",
+       "                               geometry  \n",
+       "2714578  POINT (143436.362 -420025.038)  \n",
+       "2714814  POINT (143169.561 -420048.613)  \n",
+       "2716121  POINT (143956.624 -419845.037)  \n",
+       "2716157  POINT (143668.058 -419967.744)  \n",
+       "2716193  POINT (142883.028 -420093.918)  \n",
+       "2742567  POINT (125364.090 -426918.777)  \n",
+       "2742581  POINT (127206.779 -422123.347)  \n",
+       "2742598  POINT (125847.611 -423758.251)  \n",
+       "2742612  POINT (130023.517 -422076.878)  \n",
+       "2742629  POINT (123902.277 -424437.038)  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scheduled_stop_times.query('trip_instance_key == \"0c9b170807546736de0820cf8860bc00\"').head(10) #  not sorted..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6611bb6f-e903-43d2-a282-cc4e1385cf24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trip_cols = [\"trip_instance_key\"]\n",
+    "trip_stop_cols = [\"trip_instance_key\", \"stop_sequence\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "72983c13-21f2-4ef2-b862-ebbbf985aff9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.9/site-packages/shapely/linear.py:90: RuntimeWarning: invalid value encountered in line_locate_point\n",
+      "  return lib.line_locate_point(line, other)\n"
+     ]
+    }
+   ],
+   "source": [
+    "stop_meters = sd.get_projected_stop_meters(scheduled_stop_times, analysis_date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "44f77c55-1548-4af9-b4c8-11d24f336d1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf = scheduled_stop_times[\n",
+    "    trip_stop_cols + [\"stop_id\", \"stop_name\", \"geometry\"]\n",
+    "].assign(\n",
+    "    stop_meters = stop_meters\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "423601fb-53cd-4d7d-b6ac-9ca683b81e4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_name</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>stop_meters</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2714578</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>37</td>\n",
+       "      <td>9255</td>\n",
+       "      <td>Chase / Katherine</td>\n",
+       "      <td>POINT (143436.362 -420025.038)</td>\n",
+       "      <td>20462.186863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2714814</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>38</td>\n",
+       "      <td>9256</td>\n",
+       "      <td>Chase / Lennox</td>\n",
+       "      <td>POINT (143169.561 -420048.613)</td>\n",
+       "      <td>12921.438656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2716121</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>35</td>\n",
+       "      <td>695</td>\n",
+       "      <td>Chase / Ranchito</td>\n",
+       "      <td>POINT (143956.624 -419845.037)</td>\n",
+       "      <td>1247.844911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2716157</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>36</td>\n",
+       "      <td>9254</td>\n",
+       "      <td>Chase / Hazeltine</td>\n",
+       "      <td>POINT (143668.058 -419967.744)</td>\n",
+       "      <td>5083.356481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2716193</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>39</td>\n",
+       "      <td>696</td>\n",
+       "      <td>Van Nuys / Chase</td>\n",
+       "      <td>POINT (142883.028 -420093.918)</td>\n",
+       "      <td>18517.731848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2746726</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30021</td>\n",
+       "      <td>Burbank Airport Ritc</td>\n",
+       "      <td>POINT (151920.171 -423388.371)</td>\n",
+       "      <td>36133.141573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2746822</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>118</td>\n",
+       "      <td>3325</td>\n",
+       "      <td>Mulholland / Willens</td>\n",
+       "      <td>POINT (126618.430 -428461.805)</td>\n",
+       "      <td>18103.141413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2746836</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>130</td>\n",
+       "      <td>6575</td>\n",
+       "      <td>Topanga Canyon / Erwin</td>\n",
+       "      <td>POINT (128506.351 -424953.278)</td>\n",
+       "      <td>18103.141413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2746897</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>43</td>\n",
+       "      <td>15124</td>\n",
+       "      <td>Van Nuys / Arminta</td>\n",
+       "      <td>POINT (142901.964 -421307.549)</td>\n",
+       "      <td>33286.017759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2747014</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>83</td>\n",
+       "      <td>14494</td>\n",
+       "      <td>Saticoy / Topanga Canyon</td>\n",
+       "      <td>POINT (128412.831 -422103.258)</td>\n",
+       "      <td>32720.390048</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>134 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        trip_instance_key  stop_sequence stop_id  \\\n",
+       "2714578  0c9b170807546736de0820cf8860bc00             37    9255   \n",
+       "2714814  0c9b170807546736de0820cf8860bc00             38    9256   \n",
+       "2716121  0c9b170807546736de0820cf8860bc00             35     695   \n",
+       "2716157  0c9b170807546736de0820cf8860bc00             36    9254   \n",
+       "2716193  0c9b170807546736de0820cf8860bc00             39     696   \n",
+       "...                                   ...            ...     ...   \n",
+       "2746726  0c9b170807546736de0820cf8860bc00              1   30021   \n",
+       "2746822  0c9b170807546736de0820cf8860bc00            118    3325   \n",
+       "2746836  0c9b170807546736de0820cf8860bc00            130    6575   \n",
+       "2746897  0c9b170807546736de0820cf8860bc00             43   15124   \n",
+       "2747014  0c9b170807546736de0820cf8860bc00             83   14494   \n",
+       "\n",
+       "                        stop_name                        geometry  \\\n",
+       "2714578         Chase / Katherine  POINT (143436.362 -420025.038)   \n",
+       "2714814            Chase / Lennox  POINT (143169.561 -420048.613)   \n",
+       "2716121          Chase / Ranchito  POINT (143956.624 -419845.037)   \n",
+       "2716157         Chase / Hazeltine  POINT (143668.058 -419967.744)   \n",
+       "2716193          Van Nuys / Chase  POINT (142883.028 -420093.918)   \n",
+       "...                           ...                             ...   \n",
+       "2746726      Burbank Airport Ritc  POINT (151920.171 -423388.371)   \n",
+       "2746822      Mulholland / Willens  POINT (126618.430 -428461.805)   \n",
+       "2746836    Topanga Canyon / Erwin  POINT (128506.351 -424953.278)   \n",
+       "2746897        Van Nuys / Arminta  POINT (142901.964 -421307.549)   \n",
+       "2747014  Saticoy / Topanga Canyon  POINT (128412.831 -422103.258)   \n",
+       "\n",
+       "          stop_meters  \n",
+       "2714578  20462.186863  \n",
+       "2714814  12921.438656  \n",
+       "2716121   1247.844911  \n",
+       "2716157   5083.356481  \n",
+       "2716193  18517.731848  \n",
+       "...               ...  \n",
+       "2746726  36133.141573  \n",
+       "2746822  18103.141413  \n",
+       "2746836  18103.141413  \n",
+       "2746897  33286.017759  \n",
+       "2747014  32720.390048  \n",
+       "\n",
+       "[134 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.query('trip_instance_key == \"0c9b170807546736de0820cf8860bc00\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b7c8a110-ff17-406f-9f77-1353606c873d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf2 = gdf.sort_values(trip_stop_cols) #  sort here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "25e6ef59-bca5-4895-9606-9d8452d04439",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  too slow on full gdf to add to script?\n",
+    "check_monotonic = gdf2.iloc[:100000,:].groupby(trip_cols).stop_sequence.apply(lambda x: x.is_monotonic_increasing)\n",
+    "assert check_monotonic.all(), 'gdf must be sorted by trip_instance_key, stop_sequence'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c7dc3832-298b-49f6-97ab-74098adb3799",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf3 = gdf2.assign(\n",
+    "    prior_geometry = (gdf2.groupby(trip_cols)\n",
+    "                      .geometry\n",
+    "                      .shift(1)),\n",
+    "    prior_stop_sequence = (gdf2.groupby(trip_cols)\n",
+    "                           .stop_sequence\n",
+    "                           .shift(1)),\n",
+    "    # add subseq stop info here\n",
+    "    subseq_stop_sequence = (gdf2.groupby(trip_cols)\n",
+    "                            .stop_sequence\n",
+    "                            .shift(-1)),\n",
+    "    subseq_stop_id = (gdf2.groupby(trip_cols)\n",
+    "                      .stop_id\n",
+    "                      .shift(-1)),\n",
+    "    subseq_stop_name = (gdf2.groupby(trip_cols)\n",
+    "                      .stop_name\n",
+    "                      .shift(-1)),\n",
+    ").fillna({\n",
+    "    **{c: \"\" for c in [\"subseq_stop_id\", \"subseq_stop_name\"]}\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df1d3498-12ba-40bb-b279-d31dbb90bbe5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf3.query('trip_instance_key == \"0c9b170807546736de0820cf8860bc00\"').head(3) #  looks good after sort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0da042d8-bfae-44b7-98a3-1074b6ef4825",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'stop_times_with_direction' from '/home/jovyan/data-analyses/gtfs_funnel/stop_times_with_direction.py'>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import importlib\n",
+    "importlib.reload(sd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "e474435b-455d-40a0-9103-14aa57c24b16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.9/site-packages/shapely/linear.py:90: RuntimeWarning: invalid value encountered in line_locate_point\n",
+      "  return lib.line_locate_point(line, other)\n",
+      "2025-02-12 17:10:33.733 | INFO     | stop_times_with_direction:assemble_stop_times_with_direction:212 - scheduled stop times with direction 2025-01-15: 0:06:49.325982\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_fix = sd.assemble_stop_times_with_direction(analysis_date, GTFS_DATA_DICT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "37e72495-b702-4888-9796-6c8fff1d9e21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "EXPORT_FILE = GTFS_DATA_DICT.rt_vs_schedule_tables.stop_times_direction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "7df04bd1-bbeb-42aa-99ee-6fdb251c2665",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = f\"{RT_SCHED_GCS}{EXPORT_FILE}_{analysis_date}.parquet\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "f2dffd0e-f1e9-46d1-81d5-aad9cafdd7e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feed_key</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>schedule_gtfs_dataset_key</th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>shape_array_key</th>\n",
+       "      <th>stop_name</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>stop_meters</th>\n",
+       "      <th>prior_stop_sequence</th>\n",
+       "      <th>subseq_stop_sequence</th>\n",
+       "      <th>stop_primary_direction</th>\n",
+       "      <th>stop_pair</th>\n",
+       "      <th>stop_pair_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>240254</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>30021</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Burbank Airport Ritc</td>\n",
+       "      <td>POINT (151920.171 -423388.371)</td>\n",
+       "      <td>36133.141573</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>30021__3625</td>\n",
+       "      <td>Burbank Airport Ritc__Hollywood Way / Thornton</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240255</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>3625</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Hollywood Way / Thornton</td>\n",
+       "      <td>POINT (152164.852 -423047.563)</td>\n",
+       "      <td>35377.718010</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Northbound</td>\n",
+       "      <td>3625__12116</td>\n",
+       "      <td>Hollywood Way / Thornton__Hollywood Way / Winona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240256</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>12116</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Hollywood Way / Winona</td>\n",
+       "      <td>POINT (152163.927 -422698.157)</td>\n",
+       "      <td>17547.436901</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Northbound</td>\n",
+       "      <td>12116__2499</td>\n",
+       "      <td>Hollywood Way / Winona__Hollywood Way / Tulare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240257</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>2499</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Hollywood Way / Tulare</td>\n",
+       "      <td>POINT (152163.038 -422334.989)</td>\n",
+       "      <td>5248.682609</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Northbound</td>\n",
+       "      <td>2499__3567</td>\n",
+       "      <td>Hollywood Way / Tulare__Burbank Airport North</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240258</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>3567</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Burbank Airport North</td>\n",
+       "      <td>POINT (152086.762 -422003.426)</td>\n",
+       "      <td>6869.157595</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>Northbound</td>\n",
+       "      <td>3567__3252</td>\n",
+       "      <td>Burbank Airport North__San Fernando / Lockheed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240383</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>6575</td>\n",
+       "      <td>130</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Topanga Canyon / Erwin</td>\n",
+       "      <td>POINT (128506.351 -424953.278)</td>\n",
+       "      <td>18103.141413</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>Northbound</td>\n",
+       "      <td>6575__6597</td>\n",
+       "      <td>Topanga Canyon / Erwin__Victory / Topanga Canyon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240384</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>6597</td>\n",
+       "      <td>131</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Victory / Topanga Canyon</td>\n",
+       "      <td>POINT (128552.060 -424482.483)</td>\n",
+       "      <td>13357.937722</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>Northbound</td>\n",
+       "      <td>6597__7148</td>\n",
+       "      <td>Victory / Topanga Canyon__Victory / Owensmouth</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240385</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>7148</td>\n",
+       "      <td>132</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Victory / Owensmouth</td>\n",
+       "      <td>POINT (128919.099 -424394.634)</td>\n",
+       "      <td>8608.096062</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>133.0</td>\n",
+       "      <td>Eastbound</td>\n",
+       "      <td>7148__5507</td>\n",
+       "      <td>Victory / Owensmouth__Canoga Station</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240386</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>5507</td>\n",
+       "      <td>133</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Canoga Station</td>\n",
+       "      <td>POINT (129268.917 -424046.607)</td>\n",
+       "      <td>22395.340227</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>134.0</td>\n",
+       "      <td>Eastbound</td>\n",
+       "      <td>5507__5505</td>\n",
+       "      <td>Canoga Station__Rocketdyne Layover</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240387</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>5505</td>\n",
+       "      <td>134</td>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>Rocketdyne Layover</td>\n",
+       "      <td>POINT (129268.398 -424011.552)</td>\n",
+       "      <td>8883.330427</td>\n",
+       "      <td>133.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Northbound</td>\n",
+       "      <td>5505__</td>\n",
+       "      <td>Rocketdyne Layover__</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>134 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                feed_key stop_id  stop_sequence  \\\n",
+       "240254  7a5a0fa93eb3c7a402c0887a4f11f066   30021              1   \n",
+       "240255  7a5a0fa93eb3c7a402c0887a4f11f066    3625              2   \n",
+       "240256  7a5a0fa93eb3c7a402c0887a4f11f066   12116              3   \n",
+       "240257  7a5a0fa93eb3c7a402c0887a4f11f066    2499              4   \n",
+       "240258  7a5a0fa93eb3c7a402c0887a4f11f066    3567              5   \n",
+       "...                                  ...     ...            ...   \n",
+       "240383  7a5a0fa93eb3c7a402c0887a4f11f066    6575            130   \n",
+       "240384  7a5a0fa93eb3c7a402c0887a4f11f066    6597            131   \n",
+       "240385  7a5a0fa93eb3c7a402c0887a4f11f066    7148            132   \n",
+       "240386  7a5a0fa93eb3c7a402c0887a4f11f066    5507            133   \n",
+       "240387  7a5a0fa93eb3c7a402c0887a4f11f066    5505            134   \n",
+       "\n",
+       "               schedule_gtfs_dataset_key                 trip_instance_key  \\\n",
+       "240254  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "240255  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "240256  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "240257  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "240258  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "...                                  ...                               ...   \n",
+       "240383  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "240384  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "240385  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "240386  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "240387  0666caf3ec1ecc96b74f4477ee4bc939  0c9b170807546736de0820cf8860bc00   \n",
+       "\n",
+       "                         shape_array_key                 stop_name  \\\n",
+       "240254  59459dbb9f7ce418d6f88f65e905d583      Burbank Airport Ritc   \n",
+       "240255  59459dbb9f7ce418d6f88f65e905d583  Hollywood Way / Thornton   \n",
+       "240256  59459dbb9f7ce418d6f88f65e905d583    Hollywood Way / Winona   \n",
+       "240257  59459dbb9f7ce418d6f88f65e905d583    Hollywood Way / Tulare   \n",
+       "240258  59459dbb9f7ce418d6f88f65e905d583     Burbank Airport North   \n",
+       "...                                  ...                       ...   \n",
+       "240383  59459dbb9f7ce418d6f88f65e905d583    Topanga Canyon / Erwin   \n",
+       "240384  59459dbb9f7ce418d6f88f65e905d583  Victory / Topanga Canyon   \n",
+       "240385  59459dbb9f7ce418d6f88f65e905d583      Victory / Owensmouth   \n",
+       "240386  59459dbb9f7ce418d6f88f65e905d583            Canoga Station   \n",
+       "240387  59459dbb9f7ce418d6f88f65e905d583        Rocketdyne Layover   \n",
+       "\n",
+       "                              geometry   stop_meters  prior_stop_sequence  \\\n",
+       "240254  POINT (151920.171 -423388.371)  36133.141573                  NaN   \n",
+       "240255  POINT (152164.852 -423047.563)  35377.718010                  1.0   \n",
+       "240256  POINT (152163.927 -422698.157)  17547.436901                  2.0   \n",
+       "240257  POINT (152163.038 -422334.989)   5248.682609                  3.0   \n",
+       "240258  POINT (152086.762 -422003.426)   6869.157595                  4.0   \n",
+       "...                                ...           ...                  ...   \n",
+       "240383  POINT (128506.351 -424953.278)  18103.141413                129.0   \n",
+       "240384  POINT (128552.060 -424482.483)  13357.937722                130.0   \n",
+       "240385  POINT (128919.099 -424394.634)   8608.096062                131.0   \n",
+       "240386  POINT (129268.917 -424046.607)  22395.340227                132.0   \n",
+       "240387  POINT (129268.398 -424011.552)   8883.330427                133.0   \n",
+       "\n",
+       "        subseq_stop_sequence stop_primary_direction    stop_pair  \\\n",
+       "240254                   2.0                Unknown  30021__3625   \n",
+       "240255                   3.0             Northbound  3625__12116   \n",
+       "240256                   4.0             Northbound  12116__2499   \n",
+       "240257                   5.0             Northbound   2499__3567   \n",
+       "240258                   6.0             Northbound   3567__3252   \n",
+       "...                      ...                    ...          ...   \n",
+       "240383                 131.0             Northbound   6575__6597   \n",
+       "240384                 132.0             Northbound   6597__7148   \n",
+       "240385                 133.0              Eastbound   7148__5507   \n",
+       "240386                 134.0              Eastbound   5507__5505   \n",
+       "240387                   NaN             Northbound       5505__   \n",
+       "\n",
+       "                                          stop_pair_name  \n",
+       "240254    Burbank Airport Ritc__Hollywood Way / Thornton  \n",
+       "240255  Hollywood Way / Thornton__Hollywood Way / Winona  \n",
+       "240256    Hollywood Way / Winona__Hollywood Way / Tulare  \n",
+       "240257     Hollywood Way / Tulare__Burbank Airport North  \n",
+       "240258    Burbank Airport North__San Fernando / Lockheed  \n",
+       "...                                                  ...  \n",
+       "240383  Topanga Canyon / Erwin__Victory / Topanga Canyon  \n",
+       "240384    Victory / Topanga Canyon__Victory / Owensmouth  \n",
+       "240385              Victory / Owensmouth__Canoga Station  \n",
+       "240386                Canoga Station__Rocketdyne Layover  \n",
+       "240387                              Rocketdyne Layover__  \n",
+       "\n",
+       "[134 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gpd.read_parquet(path).query('trip_instance_key == \"0c9b170807546736de0820cf8860bc00\"') #  looks good after sort"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/gtfs_funnel/Makefile b/gtfs_funnel/Makefile
index d17bc190a..a46d17b70 100644
--- a/gtfs_funnel/Makefile
+++ b/gtfs_funnel/Makefile
@@ -48,7 +48,7 @@ funnel_gtfs_single_day:
 all:
 	make funnel_gtfs_single_day
     # update open_data/update_vars.py        
-	cd ../open_data/ && make create_gtfs_schedule_geospatial_open_data -f Makefile
+#	cd ../open_data/ && make create_gtfs_schedule_geospatial_open_data -f Makefile
     # update high_quality_transit_areas/update_vars.py    
 	cd ../high_quality_transit_areas/ && make hqta_data -f Makefile    
     # update rt_segment_speeds/segment_speed_utils/project_vars.py
diff --git a/gtfs_funnel/logs/download_data.log b/gtfs_funnel/logs/download_data.log
index 833f5b529..6f9705026 100644
--- a/gtfs_funnel/logs/download_data.log
+++ b/gtfs_funnel/logs/download_data.log
@@ -694,3 +694,19 @@
 2024-12-17 13:03:01.386 | INFO     | __main__:download_one_day:29 - # operators to run: 194
 2024-12-17 13:03:01.387 | INFO     | __main__:download_one_day:33 - *********** Download st data ***********
 2024-12-17 13:04:59.321 | INFO     | __main__:download_one_day:56 - execution time: 0:02:00.030094
+2025-01-16 13:31:53.693 | INFO     | __main__:download_one_day:45 - Analysis date: 2025-01-15
+2025-01-16 13:31:57.110 | INFO     | __main__:download_one_day:52 - # operators to run: 227
+2025-01-16 13:31:57.111 | INFO     | __main__:download_one_day:56 - *********** Download trips data ***********
+2025-01-16 13:32:29.962 | INFO     | __main__:download_one_day:86 - execution time: 0:00:36.247158
+2025-01-16 13:32:48.465 | INFO     | __main__:download_one_day:22 - Analysis date: 2025-01-15
+2025-01-16 13:32:50.458 | INFO     | __main__:download_one_day:29 - # operators to run: 227
+2025-01-16 13:32:50.459 | INFO     | __main__:download_one_day:33 - *********** Download stops data ***********
+2025-01-16 13:33:00.364 | INFO     | __main__:download_one_day:64 - execution time: 0:00:11.898404
+2025-01-16 13:33:16.781 | INFO     | __main__:download_one_day:22 - Analysis date: 2025-01-15
+2025-01-16 13:33:18.779 | INFO     | __main__:download_one_day:29 - # operators to run: 227
+2025-01-16 13:33:18.779 | INFO     | __main__:download_one_day:33 - *********** Download routelines data ***********
+2025-01-16 13:35:14.243 | INFO     | __main__:download_one_day:63 - execution time: 0:01:57.461108
+2025-01-16 13:35:31.678 | INFO     | __main__:download_one_day:21 - Analysis date: 2025-01-15
+2025-01-16 13:35:33.591 | INFO     | __main__:download_one_day:29 - # operators to run: 187
+2025-01-16 13:35:33.592 | INFO     | __main__:download_one_day:33 - *********** Download st data ***********
+2025-01-16 13:37:29.411 | INFO     | __main__:download_one_day:56 - execution time: 0:01:57.732464
diff --git a/gtfs_funnel/logs/download_vp_v2.log b/gtfs_funnel/logs/download_vp_v2.log
index 52763e3dc..da38169b8 100644
--- a/gtfs_funnel/logs/download_vp_v2.log
+++ b/gtfs_funnel/logs/download_vp_v2.log
@@ -449,3 +449,14 @@
 2024-12-17 13:19:32.102 | INFO     | __main__:<module>:112 - export concatenated vp: 0:04:12.615694
 2024-12-17 13:23:34.976 | INFO     | __main__:<module>:134 - remove batched parquets
 2024-12-17 13:23:34.976 | INFO     | __main__:<module>:137 - execution time: 0:08:23.157313
+2025-01-16 13:37:50.059 | INFO     | __main__:<module>:148 - Analysis date: 2025-01-15
+2025-01-16 13:40:22.219 | INFO     | __main__:loop_through_batches_and_download_vp:111 - exported batch 0 to GCS: 0:02:32.149970
+2025-01-16 13:41:31.441 | INFO     | __main__:loop_through_batches_and_download_vp:111 - exported batch 1 to GCS: 0:01:09.219682
+2025-01-16 13:45:40.111 | INFO     | __main__:loop_through_batches_and_download_vp:111 - exported batch 2 to GCS: 0:04:08.669348
+2025-01-16 13:47:52.255 | INFO     | __main__:loop_through_batches_and_download_vp:111 - exported batch 3 to GCS: 0:02:12.137762
+2025-01-16 13:47:52.256 | INFO     | __main__:<module>:155 - execution time: 0:10:02.186184
+2025-01-16 13:48:16.039 | INFO     | __main__:<module>:97 - Analysis date: 2025-01-15
+2025-01-16 13:48:23.877 | INFO     | __main__:<module>:105 - concat and filter batched data: 0:00:07.837533
+2025-01-16 13:52:18.653 | INFO     | __main__:<module>:112 - export concatenated vp: 0:03:54.775914
+2025-01-16 13:56:18.964 | INFO     | __main__:<module>:134 - remove batched parquets
+2025-01-16 13:56:18.987 | INFO     | __main__:<module>:137 - execution time: 0:08:02.947545
diff --git a/gtfs_funnel/logs/vp_preprocessing.log b/gtfs_funnel/logs/vp_preprocessing.log
index fed013952..99b025e1f 100644
--- a/gtfs_funnel/logs/vp_preprocessing.log
+++ b/gtfs_funnel/logs/vp_preprocessing.log
@@ -309,3 +309,17 @@
 2024-12-17 14:08:13.713 | INFO     | __main__:<module>:235 - merge with original and export: 0:01:19.916578
 2024-12-17 14:08:13.714 | INFO     | __main__:<module>:236 - vp with dwell time 2024-12-11: 0:06:07.248833
 2024-12-17 14:13:50.962 | INFO     | __main__:<module>:78 - 2024-12-11: condense vp for trip 0:05:19.406518
+2025-01-16 15:00:10.192 | INFO     | __main__:pare_down_to_valid_trips:88 - pare down vp: 0:02:40.192578
+2025-01-20 20:40:27.661 | INFO     | __main__:pare_down_to_valid_trips:88 - pare down vp: 0:02:57.913529
+2025-01-20 20:45:06.455 | INFO     | __main__:pare_down_to_valid_trips:88 - pare down vp: 0:02:28.449333
+2025-01-20 20:58:21.900 | INFO     | __main__:pare_down_to_valid_trips:88 - pare down vp: 0:02:27.450257
+2025-01-20 21:06:38.791 | INFO     | __main__:pare_down_to_valid_trips:94 - export vp direction: 0:08:16.891594
+2025-01-20 21:43:35.817 | INFO     | __main__:pare_down_to_valid_trips:88 - pare down vp: 0:02:56.099553
+2025-01-20 22:19:14.276 | INFO     | __main__:pare_down_to_valid_trips:88 - pare down vp: 0:02:55.161167
+2025-01-20 22:27:27.465 | INFO     | __main__:pare_down_to_valid_trips:94 - export vp direction: 0:08:13.188856
+2025-01-20 22:29:11.131 | INFO     | __main__:merge_in_vp_direction:143 - 2025-01-15: export usable vp with direction: 0:01:35.266091
+2025-01-20 22:29:11.360 | INFO     | __main__:<module>:215 - 2025-01-15: pare down vp, add direction execution time: 0:12:52.244708
+2025-01-20 22:34:09.150 | INFO     | __main__:<module>:216 - compute dwell df: 0:04:20.527838
+2025-01-20 22:35:38.339 | INFO     | __main__:<module>:241 - merge with original and export: 0:01:29.188729
+2025-01-20 22:35:38.340 | INFO     | __main__:<module>:242 - vp with dwell time 2025-01-15: 0:05:49.716567
+2025-01-20 22:42:17.477 | INFO     | __main__:<module>:80 - 2025-01-15: condense vp for trip 0:06:20.345485
diff --git a/gtfs_funnel/published_operators.yml b/gtfs_funnel/published_operators.yml
index 2e6df4267..dc02c2e34 100644
--- a/gtfs_funnel/published_operators.yml
+++ b/gtfs_funnel/published_operators.yml
@@ -1,17 +1,19 @@
-2024-10-16:
+2024-12-11:
   - Alhambra Schedule
   - Amador Schedule
+  - Amtrak Schedule
   - Anaheim Resort Schedule
   - Antelope Valley Transit Authority Schedule
   - Arcadia Schedule
+  - Artesia Schedule
   - Arvin Schedule
   - Auburn Schedule
   - B-Line Schedule
   - Baldwin Park Schedule
   - Banning Pass Schedule
+  - Basin Transit GMV Schedule
   - Bay Area 511 AC Transit Schedule
   - Bay Area 511 ACE Schedule
-  - Bay Area 511 Angel Island-Tiburon Ferry Schedule
   - Bay Area 511 BART Schedule
   - Bay Area 511 Caltrain Schedule
   - Bay Area 511 Capitol Corridor Schedule
@@ -25,8 +27,10 @@
   - Bay Area 511 MVGO Schedule
   - Bay Area 511 Marin Schedule
   - Bay Area 511 Mission Bay Schedule
+  - Bay Area 511 Mountain View Community Shuttle Schedule
   - Bay Area 511 Muni Schedule
   - Bay Area 511 Petaluma Schedule
+  - Bay Area 511 PresidiGo Schedule
   - Bay Area 511 Rio Vista Delta Breeze Schedule
   - Bay Area 511 SFO AirTrain Schedule
   - Bay Area 511 SamTrans Schedule
@@ -46,7 +50,7 @@
   - Bay Area 511 WestCAT Schedule
   - Beach Cities GMV Schedule
   - Bear Schedule
-  - Beaumont Pass Schedule
+  - Beaumont Transit Schedule
   - Bell Gardens Schedule
   - Bellflower Bus Schedule
   - Big Blue Bus Schedule
@@ -68,6 +72,7 @@
   - Delano Schedule
   - Desert Roadrunner GMV Schedule
   - Desert Roadrunner Schedule
+  - DowneyLink Avail Schedule
   - Eastern Sierra Schedule
   - El Dorado Schedule
   - El Monte Schedule
@@ -76,9 +81,10 @@
   - Foothill Schedule
   - Fresno County Schedule
   - Fresno Schedule
+  - G Trans Schedule
   - GET Schedule
   - Get Around Town Express Schedule
-  - Glendora Schedule
+  - Glendale Schedule
   - Glenn Schedule
   - Go West Schedule
   - Grapeline Schedule
@@ -111,7 +117,6 @@
   - MV Shuttle Schedule
   - Madera County Connection Schedule
   - Madera Metro Schedule
-  - Mariposa Grove Shuttle Schedule
   - Maywood Schedule
   - Mendocino Schedule
   - Merced GMV Schedule
@@ -134,7 +139,6 @@
   - Pasadena Schedule
   - Placer Schedule
   - Plumas Schedule
-  - PresidiGo Schedule
   - Redding Schedule
   - Redwood Coast Schedule
   - Riverside Schedule
@@ -147,7 +151,6 @@
   - SLORTA Schedule
   - Sacramento Schedule
   - Sage Stage Schedule
-  - San Clemente Trolley Schedule
   - San Diego Schedule
   - San Fernando Schedule
   - San Joaquin Schedule
@@ -186,11 +189,18 @@
   - Yosemite Valley Shuttle Schedule
   - Yuba-Sutter Schedule
   - Yuma Schedule
+  - Yurok Schedule
   - eTrans Schedule
+2024-11-13:
+  - Glendora Schedule
+  - Mariposa Grove Shuttle Schedule
+  - San Clemente Trolley Schedule
+2024-10-16:
+  - Bay Area 511 Angel Island-Tiburon Ferry Schedule
+  - Beaumont Pass Schedule
+  - PresidiGo Schedule
 2024-09-18:
   - DowneyLINK GMV Schedule
-  - G Trans Schedule
-  - Glendale Schedule
 2024-06-12:
   - Anteater Express Schedule
   - Lynwood Schedule
@@ -206,7 +216,6 @@
   - DowneyLINK Schedule
   - Spirit Bus Schedule
 2023-11-15:
-  - Amtrak Schedule
   - Mission Bay Schedule
 2023-08-15:
   - Blossom Express Schedule
diff --git a/gtfs_funnel/stop_times_with_direction.py b/gtfs_funnel/stop_times_with_direction.py
index f6205d757..bdbd00fad 100644
--- a/gtfs_funnel/stop_times_with_direction.py
+++ b/gtfs_funnel/stop_times_with_direction.py
@@ -116,7 +116,10 @@ def find_prior_subseq_stop_info(
     ].assign(
         stop_meters = stop_meters
     )
-    
+    gdf = gdf.sort_values(trip_stop_cols) #  important! gdf loses sort after prep_scheduled_stop_times
+    #  slow check , commenting out since we're sorting right here (is there a faster way to test?)
+    # check_monotonic = gdf.groupby(trip_cols).stop_sequence.apply(lambda x: x.is_monotonic_increasing)
+    # assert check_monotonic.all(), 'gdf must be sorted by trip_instance_key, stop_sequence'
     gdf = gdf.assign(
         prior_geometry = (gdf.groupby(trip_cols)
                           .geometry
diff --git a/gtfs_funnel/update_vars.py b/gtfs_funnel/update_vars.py
index 1d2d5aac3..5212a3f1d 100644
--- a/gtfs_funnel/update_vars.py
+++ b/gtfs_funnel/update_vars.py
@@ -13,7 +13,8 @@
 
 
 # analysis_date_list = [rt_dates.DATES["dec2024"]] + [rt_dates.DATES['nov2024']]
-analysis_date_list = all_dates
+analysis_date_list = [rt_dates.DATES["jan2025"]]
+# analysis_date_list = all_dates
 GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data")
 
 COMPILED_CACHED_VIEWS = GTFS_DATA_DICT.gcs_paths.COMPILED_CACHED_VIEWS
diff --git a/gtfs_funnel/vp_keep_usable.py b/gtfs_funnel/vp_keep_usable.py
index f63399d82..e0036a9c1 100644
--- a/gtfs_funnel/vp_keep_usable.py
+++ b/gtfs_funnel/vp_keep_usable.py
@@ -20,9 +20,9 @@
 from calitp_data_analysis import utils
 from calitp_data_analysis.geography_utils import WGS84
 from segment_speed_utils.project_vars import PROJECT_CRS
+from segment_speed_utils import vp_transform
 from shared_utils import geo_utils, publish_utils, rt_utils
 from update_vars import GTFS_DATA_DICT, SEGMENT_GCS
-import vp_transform
 
 def find_valid_trips(
     vp: pd.DataFrame,
@@ -154,10 +154,10 @@ def get_vp_direction_column(
         ["trip_instance_key", "vp_idx", "geometry"]
     ].to_crs(PROJECT_CRS)
     
-    vp_condensed = vp_transform.condense_by_trip(
+    vp_condensed = vp_transform.condense_point_geom_to_line(
         vp_gdf,
         group_cols = ["trip_instance_key"],
-        sort_cols = ["trip_instance_key", "vp_idx"], 
+#        sort_cols = ["trip_instance_key", "vp_idx"], not used?
         array_cols = ["vp_idx", "geometry"]        
     )
     
@@ -179,10 +179,7 @@ def get_vp_direction_column(
     
     vp_condensed = vp_condensed.assign(
         vp_primary_direction = vp_direction_series
-    )[keep_cols].pipe(
-        vp_transform.explode_arrays, 
-        array_cols = keep_cols
-    )
+    )[keep_cols].explode(column=keep_cols)
     
     vp_condensed.to_parquet(
         f"{SEGMENT_GCS}vp_direction_{analysis_date}.parquet"
diff --git a/high_quality_transit_areas/Makefile b/high_quality_transit_areas/Makefile
index 40355558d..d373f296b 100644
--- a/high_quality_transit_areas/Makefile
+++ b/high_quality_transit_areas/Makefile
@@ -1,8 +1,8 @@
 hqta_data:
-#	python rail_ferry_brt_stops.py
-#	python create_hqta_segments.py
-#	python create_aggregate_stop_frequencies.py
-#	python sjoin_stops_to_segments.py
+	python rail_ferry_brt_stops.py
+	python create_hqta_segments.py
+	python create_aggregate_stop_frequencies.py
+	python sjoin_stops_to_segments.py
 	python prep_pairwise_intersections.py
 	python get_intersections.py
 	python create_bus_hqta_types.py
diff --git a/high_quality_transit_areas/logs/hqta_processing.log b/high_quality_transit_areas/logs/hqta_processing.log
index 3b3f77a3a..220e52eca 100644
--- a/high_quality_transit_areas/logs/hqta_processing.log
+++ b/high_quality_transit_areas/logs/hqta_processing.log
@@ -133,3 +133,15 @@
 2024-12-19 14:07:17.276 | INFO     | __main__:<module>:175 - C3_create_bus_hqta_types 2024-12-11 execution time: 0:00:13.000987
 2024-12-19 14:07:53.167 | INFO     | __main__:<module>:219 - D1_assemble_hqta_points 2024-12-11 execution time: 0:00:18.462037
 2024-12-19 14:08:28.603 | INFO     | __main__:<module>:168 - D2_assemble_hqta_polygons 2024-12-11 execution time: 0:00:17.899141
+2025-01-21 12:29:45.420 | INFO     | __main__:<module>:152 - C1_prep_pairwise_intersections 2025-01-15 execution time: 0:00:04.144644
+2025-01-21 12:30:11.312 | INFO     | __main__:<module>:124 - C2_find_intersections 2025-01-15 execution time: 0:00:06.889220
+2025-01-21 12:30:42.507 | INFO     | __main__:<module>:175 - C3_create_bus_hqta_types 2025-01-15 execution time: 0:00:11.787179
+2025-01-21 12:33:02.590 | INFO     | __main__:<module>:277 - A1_rail_ferry_brt_stops 2025-01-15 execution time: 0:00:21.539013
+2025-01-21 12:39:24.599 | INFO     | __main__:<module>:248 - B1_create_hqta_segments execution time: 0:06:02.040432
+2025-01-21 12:42:42.569 | INFO     | __main__:<module>:333 - B2_create_aggregate_stop_frequencies 2025-01-15 execution time: 0:02:57.592371
+2025-01-21 12:43:15.328 | INFO     | __main__:<module>:326 - B3_sjoin_stops_to_segments 2025-01-15 execution time: 0:00:13.240782
+2025-01-21 12:43:39.277 | INFO     | __main__:<module>:152 - C1_prep_pairwise_intersections 2025-01-15 execution time: 0:00:04.280915
+2025-01-21 12:44:05.244 | INFO     | __main__:<module>:124 - C2_find_intersections 2025-01-15 execution time: 0:00:07.267713
+2025-01-21 12:44:35.610 | INFO     | __main__:<module>:175 - C3_create_bus_hqta_types 2025-01-15 execution time: 0:00:11.535686
+2025-01-21 12:45:13.065 | INFO     | __main__:<module>:219 - D1_assemble_hqta_points 2025-01-15 execution time: 0:00:18.292937
+2025-01-21 12:45:46.666 | INFO     | __main__:<module>:168 - D2_assemble_hqta_polygons 2025-01-15 execution time: 0:00:14.175687
diff --git a/high_quality_transit_areas/update_vars.py b/high_quality_transit_areas/update_vars.py
index 2911e6499..372232de7 100644
--- a/high_quality_transit_areas/update_vars.py
+++ b/high_quality_transit_areas/update_vars.py
@@ -1,7 +1,7 @@
 from shared_utils import rt_dates
 import datetime as dt
 
-analysis_date = rt_dates.DATES["dec2024"]
+analysis_date = rt_dates.DATES["jan2025"]
 
 GCS_FILE_PATH = ("gs://calitp-analytics-data/data-analyses/"
                  "high_quality_transit_areas/")
diff --git a/open_data/update_vars.py b/open_data/update_vars.py
index ba7aed422..a651fa7d6 100644
--- a/open_data/update_vars.py
+++ b/open_data/update_vars.py
@@ -1,7 +1,7 @@
 from pathlib import Path
 from shared_utils import catalog_utils, rt_dates
 
-analysis_date = rt_dates.DATES["dec2024"]
+analysis_date = rt_dates.DATES["jan2025"]
 
 GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data")
 
diff --git a/portfolio/rt/README.md b/portfolio/rt/README.md
index dbca276c6..20e7626c2 100644
--- a/portfolio/rt/README.md
+++ b/portfolio/rt/README.md
@@ -45,6 +45,8 @@ We're aligining with segment speeds published via the [open data portal!](https:
 While the datasets don't fully match just yet, this site is now based on the same underlying data. This should help us update it more often, enhance accuracy for complex route shapes, and include as many transit operators as possible.
 
 The site also includes a more informative speed and variation by time of day chart at the bottom of the page.
+
+Route segments with no data are now shown as thin, dark gray or black segments on the maps. These may be due to the route not running at that time of day, or a data availability or processing issue.
     
 ## Data Sources
 Archived GTFS-Realtime Vehicle Positions data, plus corresponding GTFS Schedule data.
diff --git a/portfolio/rt/_toc.yml b/portfolio/rt/_toc.yml
index 704e6f5ef..0ea9e24bb 100644
--- a/portfolio/rt/_toc.yml
+++ b/portfolio/rt/_toc.yml
@@ -20,9 +20,9 @@ parts:
     -   file: district_06-fresno.md
         sections:
         -   glob: district_06-fresno/*
-    -   file: district_07-los-angeles.md
+    -   file: district_07-los-angeles-ventura.md
         sections:
-        -   glob: district_07-los-angeles/*
+        -   glob: district_07-los-angeles-ventura/*
     -   file: district_08-san-bernardino.md
         sections:
         -   glob: district_08-san-bernardino/*
diff --git a/portfolio/rt/district_01-eureka/00__01_new_speedmaps__district_01-eureka__organization_source_record_id_recaaoqedvwhcmivt.ipynb b/portfolio/rt/district_01-eureka/00__01_new_speedmaps__district_01-eureka__organization_source_record_id_recaaoqedvwhcmivt.ipynb
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-      - caption: District 07 - Los Angeles
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 2025-01-16 15:49:56.361 | INFO     | __main__:route_metrics:88 - route aggregation 2023-10-11: 0:00:01.825395
 2025-01-16 15:49:58.178 | INFO     | __main__:route_metrics:88 - route aggregation 2023-11-15: 0:00:01.812722
 2025-01-16 15:50:00.055 | INFO     | __main__:route_metrics:88 - route aggregation 2023-12-13: 0:00:01.873527
+2025-01-22 09:30:07.117 | INFO     | __main__:route_metrics:85 - route aggregation 2025-01-15: 0:00:03.343220
diff --git a/rt_scheduled_v_ran/logs/rt_v_scheduled_trip_metrics.log b/rt_scheduled_v_ran/logs/rt_v_scheduled_trip_metrics.log
index f999d3b8d..816309333 100644
--- a/rt_scheduled_v_ran/logs/rt_v_scheduled_trip_metrics.log
+++ b/rt_scheduled_v_ran/logs/rt_v_scheduled_trip_metrics.log
@@ -483,3 +483,6 @@
 2024-12-19 09:26:56.119 | INFO     | __main__:rt_schedule_trip_metrics:280 - tabular trip metrics 2024-12-11: 0:03:23.206651
 2024-12-19 09:51:46.617 | INFO     | __main__:rt_schedule_trip_metrics:285 - spatial trip metrics 2024-12-11: 0:24:50.497597
 2024-12-19 09:53:04.131 | INFO     | __main__:rt_schedule_trip_metrics:333 - Total run time for metrics on 2024-12-11: 0:29:31.218478
+2025-01-22 09:01:53.774 | INFO     | __main__:rt_schedule_trip_metrics:280 - tabular trip metrics 2025-01-15: 0:02:57.352651
+2025-01-22 09:27:32.424 | INFO     | __main__:rt_schedule_trip_metrics:285 - spatial trip metrics 2025-01-15: 0:25:38.649928
+2025-01-22 09:29:00.238 | INFO     | __main__:rt_schedule_trip_metrics:333 - Total run time for metrics on 2025-01-15: 0:30:03.817015
diff --git a/rt_scheduled_v_ran/scripts/update_vars.py b/rt_scheduled_v_ran/scripts/update_vars.py
index c000ab20a..106c5a5e6 100644
--- a/rt_scheduled_v_ran/scripts/update_vars.py
+++ b/rt_scheduled_v_ran/scripts/update_vars.py
@@ -7,8 +7,8 @@
 oct2024_week = rt_dates.get_week("oct2024", exclude_wed=True)
 
 
-# analysis_date_list = [rt_dates.DATES["nov2024"]]
-analysis_date_list = rt_dates.y2024_dates + rt_dates.y2023_dates
+analysis_date_list = [rt_dates.DATES["jan2025"]]
+# analysis_date_list = rt_dates.y2024_dates + rt_dates.y2023_dates
 
 GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data")
 
diff --git a/rt_segment_speeds/40_speedmap_scratchpad.ipynb b/rt_segment_speeds/40_speedmap_scratchpad.ipynb
deleted file mode 100644
index f405efc55..000000000
--- a/rt_segment_speeds/40_speedmap_scratchpad.ipynb
+++ /dev/null
@@ -1,686 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d528f399-cf33-459e-b5dc-92e0b4ae389a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import geopandas as gpd\n",
-    "from siuba import *\n",
-    "import shared_utils\n",
-    "\n",
-    "from calitp_data_analysis import get_fs\n",
-    "from segment_speed_utils import helpers, time_series_utils, gtfs_schedule_wrangling\n",
-    "from segment_speed_utils.project_vars import SCHED_GCS, SEGMENT_GCS, GTFS_DATA_DICT, analysis_date"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82c092ac-157e-435c-be60-2f86a11a996e",
-   "metadata": {},
-   "source": [
-    "i'd start with 5, checking if the columns you want are already there in this file (saved out at the end of stop_arrivals_to_speed, and is a speeds by trip table for that day)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6cd318e5-5c4e-4249-b158-232f4d940770",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "catalog = shared_utils.catalog_utils.get_catalog('gtfs_analytics_data')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6e86c1a8-d3de-4fc6-9478-c03133b6745c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'dir': '${gcs_paths.SEGMENT_GCS}', 'stage1': '${speeds_tables.vp_dwell}', 'proxy_stop_times': 'stop_time_expansion/speedmap_stop_times', 'stage2': 'nearest/nearest_vp_speedmap_proxy', 'stage2b': 'nearest/nearest2_vp_speedmap_proxy', 'stage3': 'speedmap/stop_arrivals_proxy', 'stage3b': 'speedmap/stop_arrivals', 'stage4': 'speedmap/speeds', 'trip_stop_cols': ['trip_instance_key', 'stop_sequence', 'stop_sequence1'], 'shape_stop_cols': ['shape_array_key', 'shape_id'], 'stop_pair_cols': ['stop_pair', 'stop_pair_name', 'segment_id'], 'route_dir_cols': ['route_id', 'direction_id'], 'segments_file': 'segment_options/speedmap_segments', 'shape_stop_single_segment': 'rollup_singleday/speeds_shape_speedmap_segments', 'shape_stop_single_segment_detail': 'rollup_singleday/speeds_shape_speedmap_segments_detail', 'route_dir_single_segment': 'rollup_singleday/speeds_route_dir_speedmap_segments', 'route_dir_multi_segment': 'rollup_multiday/speeds_route_dir_speedmap_segments', 'min_trip_minutes': '${speed_vars.time_min_cutoff}', 'max_trip_minutes': 180, 'max_speed': '${speed_vars.max_speed}', 'min_meters_elapsed': 1609, 'segment_meters': 1000}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "catalog.speedmap_segments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "f766b50a-c70d-4681-992c-d7f8979ada69",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2024-10-16'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "analysis_date"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "b4dd7af7-0cfa-4d94-a96d-f11e7cdc12f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# path = f'{catalog.speedmap_segments.dir}{catalog.speedmap_segments.stage4}_{analysis_date}.parquet'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "12b824f2-4326-4f5e-ae86-95a46703c948",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = f'{catalog.speedmap_segments.dir}{catalog.speedmap_segments.shape_stop_single_segment_detail}_{analysis_date}.parquet'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2743cbe7-b472-4ea2-af08-13d2003e93db",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'gs://calitp-analytics-data/data-analyses/rt_segment_speeds/rollup_singleday/speeds_shape_speedmap_segments_detail_2024-10-16.parquet'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7669d09d-c49e-448c-b57a-299112614bd0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "detail = gpd.read_parquet(path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ddf376af-9781-4943-b0d7-5f5418e07735",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>schedule_gtfs_dataset_key</th>\n",
-       "      <th>shape_array_key</th>\n",
-       "      <th>shape_id</th>\n",
-       "      <th>route_id</th>\n",
-       "      <th>route_short_name</th>\n",
-       "      <th>stop_pair</th>\n",
-       "      <th>stop_pair_name</th>\n",
-       "      <th>segment_id</th>\n",
-       "      <th>time_of_day</th>\n",
-       "      <th>p50_mph</th>\n",
-       "      <th>...</th>\n",
-       "      <th>p20_mph</th>\n",
-       "      <th>p80_mph</th>\n",
-       "      <th>name</th>\n",
-       "      <th>caltrans_district</th>\n",
-       "      <th>organization_source_record_id</th>\n",
-       "      <th>organization_name</th>\n",
-       "      <th>base64_url</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>n_trips_sch</th>\n",
-       "      <th>trips_hr_sch</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0139b1253130b33adcd4b3a4490530d2</td>\n",
-       "      <td>51530f7b5ba4cdde7c644c14f262005c</td>\n",
-       "      <td>b0c096a2-4c32-4675-b005-43b58b3781e6</td>\n",
-       "      <td>874b0dfa-ab3d-47da-ac75-5fd7b9bdea42</td>\n",
-       "      <td>C30 WB</td>\n",
-       "      <td>0d1ef8b5-90e4-40de-b37b-504cb862d18b__5b9a7352...</td>\n",
-       "      <td>Woodlake Park EB__Cypress St &amp; Naranjo Blvd WB</td>\n",
-       "      <td>0d1ef8b5-90e4-40de-b37b-504cb862d18b-5b9a7352-...</td>\n",
-       "      <td>AM Peak</td>\n",
-       "      <td>17.27</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15.20</td>\n",
-       "      <td>20.96</td>\n",
-       "      <td>TCRTA TripShot Schedule</td>\n",
-       "      <td>06 - Fresno</td>\n",
-       "      <td>rec4YWVqVwJ9XqDbq</td>\n",
-       "      <td>Tulare County Regional Transit Agency</td>\n",
-       "      <td>aHR0cHM6Ly90Y3J0YS50cmlwc2hvdC5jb20vdjEvZ3Rmcy...</td>\n",
-       "      <td>LINESTRING (-119.09762 36.41481, -119.09762 36...</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0139b1253130b33adcd4b3a4490530d2</td>\n",
-       "      <td>51530f7b5ba4cdde7c644c14f262005c</td>\n",
-       "      <td>b0c096a2-4c32-4675-b005-43b58b3781e6</td>\n",
-       "      <td>874b0dfa-ab3d-47da-ac75-5fd7b9bdea42</td>\n",
-       "      <td>C30 WB</td>\n",
-       "      <td>5b9a7352-556f-4272-b663-22f9821d6a40__5f894da7...</td>\n",
-       "      <td>Cypress St &amp; Naranjo Blvd WB__Millwood and Roa...</td>\n",
-       "      <td>5b9a7352-556f-4272-b663-22f9821d6a40-5f894da7-...</td>\n",
-       "      <td>AM Peak</td>\n",
-       "      <td>49.50</td>\n",
-       "      <td>...</td>\n",
-       "      <td>43.85</td>\n",
-       "      <td>51.81</td>\n",
-       "      <td>TCRTA TripShot Schedule</td>\n",
-       "      <td>06 - Fresno</td>\n",
-       "      <td>rec4YWVqVwJ9XqDbq</td>\n",
-       "      <td>Tulare County Regional Transit Agency</td>\n",
-       "      <td>aHR0cHM6Ly90Y3J0YS50cmlwc2hvdC5jb20vdjEvZ3Rmcy...</td>\n",
-       "      <td>LINESTRING (-119.10550 36.41377, -119.10562 36...</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0139b1253130b33adcd4b3a4490530d2</td>\n",
-       "      <td>51530f7b5ba4cdde7c644c14f262005c</td>\n",
-       "      <td>b0c096a2-4c32-4675-b005-43b58b3781e6</td>\n",
-       "      <td>874b0dfa-ab3d-47da-ac75-5fd7b9bdea42</td>\n",
-       "      <td>C30 WB</td>\n",
-       "      <td>5b9a7352-556f-4272-b663-22f9821d6a40__5f894da7...</td>\n",
-       "      <td>Cypress St &amp; Naranjo Blvd WB__Millwood and Roa...</td>\n",
-       "      <td>5b9a7352-556f-4272-b663-22f9821d6a40-5f894da7-...</td>\n",
-       "      <td>AM Peak</td>\n",
-       "      <td>58.87</td>\n",
-       "      <td>...</td>\n",
-       "      <td>57.15</td>\n",
-       "      <td>59.50</td>\n",
-       "      <td>TCRTA TripShot Schedule</td>\n",
-       "      <td>06 - Fresno</td>\n",
-       "      <td>rec4YWVqVwJ9XqDbq</td>\n",
-       "      <td>Tulare County Regional Transit Agency</td>\n",
-       "      <td>aHR0cHM6Ly90Y3J0YS50cmlwc2hvdC5jb20vdjEvZ3Rmcy...</td>\n",
-       "      <td>LINESTRING (-119.11666 36.41379, -119.11684 36...</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.333333</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          schedule_gtfs_dataset_key                   shape_array_key  \\\n",
-       "0  0139b1253130b33adcd4b3a4490530d2  51530f7b5ba4cdde7c644c14f262005c   \n",
-       "1  0139b1253130b33adcd4b3a4490530d2  51530f7b5ba4cdde7c644c14f262005c   \n",
-       "2  0139b1253130b33adcd4b3a4490530d2  51530f7b5ba4cdde7c644c14f262005c   \n",
-       "\n",
-       "                               shape_id                              route_id  \\\n",
-       "0  b0c096a2-4c32-4675-b005-43b58b3781e6  874b0dfa-ab3d-47da-ac75-5fd7b9bdea42   \n",
-       "1  b0c096a2-4c32-4675-b005-43b58b3781e6  874b0dfa-ab3d-47da-ac75-5fd7b9bdea42   \n",
-       "2  b0c096a2-4c32-4675-b005-43b58b3781e6  874b0dfa-ab3d-47da-ac75-5fd7b9bdea42   \n",
-       "\n",
-       "  route_short_name                                          stop_pair  \\\n",
-       "0           C30 WB  0d1ef8b5-90e4-40de-b37b-504cb862d18b__5b9a7352...   \n",
-       "1           C30 WB  5b9a7352-556f-4272-b663-22f9821d6a40__5f894da7...   \n",
-       "2           C30 WB  5b9a7352-556f-4272-b663-22f9821d6a40__5f894da7...   \n",
-       "\n",
-       "                                      stop_pair_name  \\\n",
-       "0     Woodlake Park EB__Cypress St & Naranjo Blvd WB   \n",
-       "1  Cypress St & Naranjo Blvd WB__Millwood and Roa...   \n",
-       "2  Cypress St & Naranjo Blvd WB__Millwood and Roa...   \n",
-       "\n",
-       "                                          segment_id time_of_day  p50_mph  \\\n",
-       "0  0d1ef8b5-90e4-40de-b37b-504cb862d18b-5b9a7352-...     AM Peak    17.27   \n",
-       "1  5b9a7352-556f-4272-b663-22f9821d6a40-5f894da7-...     AM Peak    49.50   \n",
-       "2  5b9a7352-556f-4272-b663-22f9821d6a40-5f894da7-...     AM Peak    58.87   \n",
-       "\n",
-       "   ...  p20_mph  p80_mph                     name caltrans_district  \\\n",
-       "0  ...    15.20    20.96  TCRTA TripShot Schedule       06 - Fresno   \n",
-       "1  ...    43.85    51.81  TCRTA TripShot Schedule       06 - Fresno   \n",
-       "2  ...    57.15    59.50  TCRTA TripShot Schedule       06 - Fresno   \n",
-       "\n",
-       "  organization_source_record_id                      organization_name  \\\n",
-       "0             rec4YWVqVwJ9XqDbq  Tulare County Regional Transit Agency   \n",
-       "1             rec4YWVqVwJ9XqDbq  Tulare County Regional Transit Agency   \n",
-       "2             rec4YWVqVwJ9XqDbq  Tulare County Regional Transit Agency   \n",
-       "\n",
-       "                                          base64_url  \\\n",
-       "0  aHR0cHM6Ly90Y3J0YS50cmlwc2hvdC5jb20vdjEvZ3Rmcy...   \n",
-       "1  aHR0cHM6Ly90Y3J0YS50cmlwc2hvdC5jb20vdjEvZ3Rmcy...   \n",
-       "2  aHR0cHM6Ly90Y3J0YS50cmlwc2hvdC5jb20vdjEvZ3Rmcy...   \n",
-       "\n",
-       "                                            geometry n_trips_sch  trips_hr_sch  \n",
-       "0  LINESTRING (-119.09762 36.41481, -119.09762 36...           4      1.333333  \n",
-       "1  LINESTRING (-119.10550 36.41377, -119.10562 36...           4      1.333333  \n",
-       "2  LINESTRING (-119.11666 36.41379, -119.11684 36...           4      1.333333  \n",
-       "\n",
-       "[3 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "detail >> head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "294b8f67-1486-48e9-8fe9-015b009d8e53",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = f'{catalog.schedule_downloads.dir}{catalog.schedule_downloads.trips}_{analysis_date}.parquet'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "508d38d4-086c-43e6-8044-6ded0882e105",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'gs://calitp-analytics-data/data-analyses/rt_delay/compiled_cached_views/trips_2024-10-16.parquet'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "dc3543ea-2544-4917-887e-9de4441fb460",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trips = pd.read_parquet(path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "de097b6e-9b83-4d3a-b3e6-3646c7c7be86",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = gtfs_schedule_wrangling.get_sched_trips_hr(analysis_date)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "55d704c5-1a87-4bbf-bcde-e867b53c3988",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>route_id</th>\n",
-       "      <th>shape_id</th>\n",
-       "      <th>time_of_day</th>\n",
-       "      <th>schedule_gtfs_dataset_key</th>\n",
-       "      <th>n_trips</th>\n",
-       "      <th>trips_hr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>001</td>\n",
-       "      <td>0010111</td>\n",
-       "      <td>AM Peak</td>\n",
-       "      <td>9809d3f8121513057bc5cb8de7b54ce2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>001</td>\n",
-       "      <td>0010111</td>\n",
-       "      <td>Early AM</td>\n",
-       "      <td>9809d3f8121513057bc5cb8de7b54ce2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>001</td>\n",
-       "      <td>0010111</td>\n",
-       "      <td>Midday</td>\n",
-       "      <td>9809d3f8121513057bc5cb8de7b54ce2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  route_id shape_id time_of_day         schedule_gtfs_dataset_key  n_trips  \\\n",
-       "0      001  0010111     AM Peak  9809d3f8121513057bc5cb8de7b54ce2        3   \n",
-       "1      001  0010111    Early AM  9809d3f8121513057bc5cb8de7b54ce2        1   \n",
-       "2      001  0010111      Midday  9809d3f8121513057bc5cb8de7b54ce2        5   \n",
-       "\n",
-       "   trips_hr  \n",
-       "0  1.000000  \n",
-       "1  0.333333  \n",
-       "2  1.000000  "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df >> head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "c6f784df-8dc0-4990-aaad-4a42af5b7e5e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>route_id</th>\n",
-       "      <th>shape_id</th>\n",
-       "      <th>time_of_day</th>\n",
-       "      <th>schedule_gtfs_dataset_key</th>\n",
-       "      <th>n_trips</th>\n",
-       "      <th>trips_hr</th>\n",
-       "      <th>route_short_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>001</td>\n",
-       "      <td>0010111</td>\n",
-       "      <td>AM Peak</td>\n",
-       "      <td>9809d3f8121513057bc5cb8de7b54ce2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>001</td>\n",
-       "      <td>0010111</td>\n",
-       "      <td>Early AM</td>\n",
-       "      <td>9809d3f8121513057bc5cb8de7b54ce2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>001</td>\n",
-       "      <td>0010111</td>\n",
-       "      <td>Midday</td>\n",
-       "      <td>9809d3f8121513057bc5cb8de7b54ce2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>001</td>\n",
-       "      <td>0010111</td>\n",
-       "      <td>PM Peak</td>\n",
-       "      <td>9809d3f8121513057bc5cb8de7b54ce2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>001</td>\n",
-       "      <td>0010113</td>\n",
-       "      <td>AM Peak</td>\n",
-       "      <td>9809d3f8121513057bc5cb8de7b54ce2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19176</th>\n",
-       "      <td>shrdserv</td>\n",
-       "      <td>shrdserv_loop_3</td>\n",
-       "      <td>PM Peak</td>\n",
-       "      <td>a8d5f90bfd689badb7e1deb041408e96</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>Shared Services</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19177</th>\n",
-       "      <td>valleywide</td>\n",
-       "      <td>valleywide</td>\n",
-       "      <td>AM Peak</td>\n",
-       "      <td>31152914d10e2d0977b8b2fabb167922</td>\n",
-       "      <td>12</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19178</th>\n",
-       "      <td>valleywide</td>\n",
-       "      <td>valleywide</td>\n",
-       "      <td>Evening</td>\n",
-       "      <td>31152914d10e2d0977b8b2fabb167922</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1.800000</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19179</th>\n",
-       "      <td>valleywide</td>\n",
-       "      <td>valleywide</td>\n",
-       "      <td>Midday</td>\n",
-       "      <td>31152914d10e2d0977b8b2fabb167922</td>\n",
-       "      <td>20</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19180</th>\n",
-       "      <td>valleywide</td>\n",
-       "      <td>valleywide</td>\n",
-       "      <td>PM Peak</td>\n",
-       "      <td>31152914d10e2d0977b8b2fabb167922</td>\n",
-       "      <td>20</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>19181 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         route_id         shape_id time_of_day  \\\n",
-       "0             001          0010111     AM Peak   \n",
-       "1             001          0010111    Early AM   \n",
-       "2             001          0010111      Midday   \n",
-       "3             001          0010111     PM Peak   \n",
-       "4             001          0010113     AM Peak   \n",
-       "...           ...              ...         ...   \n",
-       "19176    shrdserv  shrdserv_loop_3     PM Peak   \n",
-       "19177  valleywide       valleywide     AM Peak   \n",
-       "19178  valleywide       valleywide     Evening   \n",
-       "19179  valleywide       valleywide      Midday   \n",
-       "19180  valleywide       valleywide     PM Peak   \n",
-       "\n",
-       "              schedule_gtfs_dataset_key  n_trips  trips_hr route_short_name  \n",
-       "0      9809d3f8121513057bc5cb8de7b54ce2        3  1.000000                1  \n",
-       "1      9809d3f8121513057bc5cb8de7b54ce2        1  0.333333                1  \n",
-       "2      9809d3f8121513057bc5cb8de7b54ce2        5  1.000000                1  \n",
-       "3      9809d3f8121513057bc5cb8de7b54ce2        3  0.600000                1  \n",
-       "4      9809d3f8121513057bc5cb8de7b54ce2        3  1.000000                1  \n",
-       "...                                 ...      ...       ...              ...  \n",
-       "19176  a8d5f90bfd689badb7e1deb041408e96        1  0.200000  Shared Services  \n",
-       "19177  31152914d10e2d0977b8b2fabb167922       12  4.000000             None  \n",
-       "19178  31152914d10e2d0977b8b2fabb167922        9  1.800000             None  \n",
-       "19179  31152914d10e2d0977b8b2fabb167922       20  4.000000             None  \n",
-       "19180  31152914d10e2d0977b8b2fabb167922       20  4.000000             None  \n",
-       "\n",
-       "[19181 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gtfs_schedule_wrangling.merge_route_identifiers(df, analysis_date)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a062796-ae2f-4081-b73b-8923c7bd85b2",
-   "metadata": {},
-   "source": [
-    "## Where'd the J line go??\n",
-    "\n",
-    "Has no route_short_name, removed that from grouping cols and added at end"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.13"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/rt_segment_speeds/44_excluded_shapes.ipynb b/rt_segment_speeds/44_excluded_shapes.ipynb
new file mode 100644
index 000000000..ffc24ea77
--- /dev/null
+++ b/rt_segment_speeds/44_excluded_shapes.ipynb
@@ -0,0 +1,511 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d528f399-cf33-459e-b5dc-92e0b4ae389a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "from siuba import *\n",
+    "import shared_utils\n",
+    "\n",
+    "from calitp_data_analysis import get_fs, geography_utils\n",
+    "from segment_speed_utils import helpers, time_series_utils, gtfs_schedule_wrangling\n",
+    "from segment_speed_utils.project_vars import SCHED_GCS, SEGMENT_GCS, GTFS_DATA_DICT, analysis_date"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83bf87cf-a9f5-4acd-afa6-d0d8920f4a73",
+   "metadata": {},
+   "source": [
+    "# Scratchpad for displaying excluded shapes on speedmaps\n",
+    "\n",
+    "* implemented via `ca_transit_speedmaps/speedmap_utils.py` for now\n",
+    "* TODO: more systematic/clarify cause as speed data gap or shape not running in time period"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cd318e5-5c4e-4249-b158-232f4d940770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "catalog = shared_utils.catalog_utils.get_catalog('gtfs_analytics_data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e86c1a8-d3de-4fc6-9478-c03133b6745c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "catalog.speedmap_segments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f766b50a-c70d-4681-992c-d7f8979ada69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82598ccf-876a-4ab2-959c-7af1b120e1e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "helpers.import_scheduled_shapes?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2e5a320-d728-49c6-99e6-a1af00c7a37c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes = helpers.import_scheduled_shapes(analysis_date, columns=['shape_array_key', 'geometry'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a652a6d-bf87-4f6c-a6ba-ab65d7bd029f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trips = helpers.import_scheduled_trips(analysis_date, columns=['shape_array_key', 'shape_id', 'route_id',\n",
+    "                                                               'route_short_name', 'gtfs_dataset_key']).drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82e1e9ee-78b1-4c32-9adb-be8b8b9639f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes = shapes.merge(trips, on='shape_array_key')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "756f4b22-e55f-40d8-ac5b-b4f493787ac2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "helpers.import_scheduled_trips?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baa0be3a-5171-4915-92a9-b83f6bd1485e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = f\"{catalog.speedmap_segments.dir}{catalog.speedmap_segments.shape_stop_single_segment_detail}_{analysis_date}.parquet\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e340a558-84ca-4d23-8758-983169f58ad3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98022a2b-46c2-46ab-b4f0-774235b81663",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf = gpd.read_parquet(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f572c39-8380-460a-a013-f23129d3c3bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "598ce854-dbfa-4a75-9002-ff284f1b6bdf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>organization_name</th>\n",
+       "      <th>organization_source_record_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>base64_url</th>\n",
+       "      <th>caltrans_district</th>\n",
+       "      <th>status</th>\n",
+       "      <th>analysis_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>City and County of San Francisco</td>\n",
+       "      <td>rechaapWbeffO33OX</td>\n",
+       "      <td>Bay Area 511 Muni Schedule</td>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L2RhdGFmZW...</td>\n",
+       "      <td>04 - Oakland</td>\n",
+       "      <td>speedmap_segs_available</td>\n",
+       "      <td>2024-12-11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   organization_name organization_source_record_id  \\\n",
+       "55  City and County of San Francisco             rechaapWbeffO33OX   \n",
+       "\n",
+       "                          name  \\\n",
+       "55  Bay Area 511 Muni Schedule   \n",
+       "\n",
+       "                                           base64_url caltrans_district  \\\n",
+       "55  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L2RhdGFmZW...      04 - Oakland   \n",
+       "\n",
+       "                     status analysis_date  \n",
+       "55  speedmap_segs_available    2024-12-11  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_parquet('../ca_transit_speed_maps/_rt_progress_2024-12-11.parquet').query('name.str.contains(\"Muni\")')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04f6fe44-dcbf-4783-a66a-2d71ee484497",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reciep8muucosbvbz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "702f4416-749b-4ef0-8f83-c6c407c4b8c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>organization_name</th>\n",
+       "      <th>organization_source_record_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>base64_url</th>\n",
+       "      <th>caltrans_district</th>\n",
+       "      <th>status</th>\n",
+       "      <th>analysis_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>City of Santa Clarita</td>\n",
+       "      <td>recImm5SmW2zq9rMT</td>\n",
+       "      <td>Santa Clarita Schedule</td>\n",
+       "      <td>aHR0cHM6Ly9hcHBzLnNhbnRhY2xhcml0YXRyYW5zaXQuY2...</td>\n",
+       "      <td>07 - Los Angeles / Ventura</td>\n",
+       "      <td>speedmap_segs_available</td>\n",
+       "      <td>2024-12-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Golden Empire Transit District</td>\n",
+       "      <td>recIh3vq8jwuuJlvL</td>\n",
+       "      <td>GET Schedule</td>\n",
+       "      <td>aHR0cDovL2V0YS5nZXRidXMub3JnL3J0dC9wdWJsaWMvdX...</td>\n",
+       "      <td>06 - Fresno</td>\n",
+       "      <td>speedmap_segs_available</td>\n",
+       "      <td>2024-12-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Mountain View Transportation Management Associ...</td>\n",
+       "      <td>recIeP8mUucOsbvbz</td>\n",
+       "      <td>Bay Area 511 MVGO Schedule</td>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L2RhdGFmZW...</td>\n",
+       "      <td>04 - Oakland</td>\n",
+       "      <td>speedmap_segs_available</td>\n",
+       "      <td>2024-12-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>Western Contra Costa Transit Authority</td>\n",
+       "      <td>recIKnsnTdKQ0vsiv</td>\n",
+       "      <td>Bay Area 511 WestCAT Schedule</td>\n",
+       "      <td>aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L2RhdGFmZW...</td>\n",
+       "      <td>04 - Oakland</td>\n",
+       "      <td>speedmap_segs_available</td>\n",
+       "      <td>2024-12-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>Redding Area Bus Authority</td>\n",
+       "      <td>recI1fuNpr306H0hw</td>\n",
+       "      <td>Redding Schedule</td>\n",
+       "      <td>aHR0cHM6Ly9ob3N0ZWQtZ3Rmcy1mZWVkcy5zMy5hbWF6b2...</td>\n",
+       "      <td>02 - Redding</td>\n",
+       "      <td>speedmap_segs_available</td>\n",
+       "      <td>2024-12-11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    organization_name  \\\n",
+       "27                              City of Santa Clarita   \n",
+       "33                     Golden Empire Transit District   \n",
+       "44  Mountain View Transportation Management Associ...   \n",
+       "53             Western Contra Costa Transit Authority   \n",
+       "65                         Redding Area Bus Authority   \n",
+       "\n",
+       "   organization_source_record_id                           name  \\\n",
+       "27             recImm5SmW2zq9rMT         Santa Clarita Schedule   \n",
+       "33             recIh3vq8jwuuJlvL                   GET Schedule   \n",
+       "44             recIeP8mUucOsbvbz     Bay Area 511 MVGO Schedule   \n",
+       "53             recIKnsnTdKQ0vsiv  Bay Area 511 WestCAT Schedule   \n",
+       "65             recI1fuNpr306H0hw               Redding Schedule   \n",
+       "\n",
+       "                                           base64_url  \\\n",
+       "27  aHR0cHM6Ly9hcHBzLnNhbnRhY2xhcml0YXRyYW5zaXQuY2...   \n",
+       "33  aHR0cDovL2V0YS5nZXRidXMub3JnL3J0dC9wdWJsaWMvdX...   \n",
+       "44  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L2RhdGFmZW...   \n",
+       "53  aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L2RhdGFmZW...   \n",
+       "65  aHR0cHM6Ly9ob3N0ZWQtZ3Rmcy1mZWVkcy5zMy5hbWF6b2...   \n",
+       "\n",
+       "             caltrans_district                   status analysis_date  \n",
+       "27  07 - Los Angeles / Ventura  speedmap_segs_available    2024-12-11  \n",
+       "33                 06 - Fresno  speedmap_segs_available    2024-12-11  \n",
+       "44                04 - Oakland  speedmap_segs_available    2024-12-11  \n",
+       "53                04 - Oakland  speedmap_segs_available    2024-12-11  \n",
+       "65                02 - Redding  speedmap_segs_available    2024-12-11  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_parquet('../ca_transit_speed_maps/_rt_progress_2024-12-11.parquet').query('organization_source_record_id.str.contains(\"recI\")')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "765c2082-8362-4719-b8f7-2760564426aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_speeds = gdf.query('organization_source_record_id == \"rechaapWbeffO33OX\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4e3cb12-fc35-45a1-819c-c8edd7c147da",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "sf_speeds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65df0527-dff2-4170-84e2-33f57d6b6ac2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_shapes = shapes.query('schedule_gtfs_dataset_key == \"7cc0cb1871dfd558f11a2885c145d144\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6710343-3567-4e68-8f5d-df221cf30d23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_shapes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4365b44-c3ae-4cfc-959c-6e6d03a1e11f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_am = sf_speeds.query('time_of_day == \"AM Peak\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71d26c00-ead6-4fbe-a159-b21ddecf35a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_am = sf_am.to_crs(geography_utils.CA_NAD83Albers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a23deac-7ce0-4703-86c4-b7008a91dc9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## shift to right side of road to display direction\n",
+    "sf_am.geometry = sf_am.geometry.apply(shared_utils.rt_utils.try_parallel)\n",
+    "sf_am = sf_am.apply(shared_utils.rt_utils.arrowize_by_frequency, axis=1, frequency_col='trips_hr_sch')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fdf4b78-f5e9-4bdf-a9eb-f33696b03aff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_am = sf_am.dissolve()\n",
+    "\n",
+    "sf_am.geometry = sf_am.buffer(35)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d68511b-1eb0-494c-ba12-bc9d8f187e78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_am.explore()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d53eaf2e-7cdb-483c-9f40-c9d0304f4d7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf = sf_shapes.overlay(sf_am, how='difference')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec1bbcee-b29b-431b-9552-1c9d97207053",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.explore()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/rt_segment_speeds/45_diff_tables.ipynb b/rt_segment_speeds/45_diff_tables.ipynb
new file mode 100644
index 000000000..a5b4ef868
--- /dev/null
+++ b/rt_segment_speeds/45_diff_tables.ipynb
@@ -0,0 +1,484 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d528f399-cf33-459e-b5dc-92e0b4ae389a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "from siuba import *\n",
+    "import shared_utils\n",
+    "\n",
+    "from calitp_data_analysis import get_fs, geography_utils\n",
+    "from segment_speed_utils import helpers, time_series_utils, gtfs_schedule_wrangling\n",
+    "from segment_speed_utils.project_vars import SCHED_GCS, SEGMENT_GCS, GTFS_DATA_DICT, analysis_date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "42a16a99-113f-48e9-9f8e-f5cea66952ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dask.dataframe as dd\n",
+    "import dask_geopandas as dg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83bf87cf-a9f5-4acd-afa6-d0d8920f4a73",
+   "metadata": {},
+   "source": [
+    "# What's up with January?\n",
+    "\n",
+    "* Follow Tiffany suggestion to look at speed tables...\n",
+    "* https://github.com/cal-itp/data-analyses/pull/1356"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6cd318e5-5c4e-4249-b158-232f4d940770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "catalog = shared_utils.catalog_utils.get_catalog('gtfs_analytics_data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02f9dbd8-cc31-4a7e-bccc-f5241b9144bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vp_tables = [key for key in catalog.speeds_tables.keys() if 'vp' in key]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "292a5d47-daaf-47bf-976f-0f1b8e28cc77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['raw_vp', 'usable_vp', 'vp_dwell', 'vp_condensed_line']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vp_tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f7d06216-4755-44e2-afd3-2a2b8ac2e1f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "paths = [catalog.speeds_tables[key] for key in catalog.speeds_tables.keys() if 'vp' in key]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3fa5e88a-16a6-4556-8d32-af691bcbbc7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['vp', 'vp_usable', 'vp_usable_dwell', 'condensed/vp_condensed']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8d30e38f-a1af-4528-8f9c-e0cf367e1623",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def diff_tables(paths: list, dates: tuple, gcs_dir = catalog.speeds_tables.dir):\n",
+    "    for table in paths:\n",
+    "        for date in dates:\n",
+    "            try:\n",
+    "                path = f'{gcs_dir}{table}_{date}.parquet'\n",
+    "                df = pd.read_parquet(path)\n",
+    "                print(path)\n",
+    "                print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\n",
+    "                print(df.info())\n",
+    "                print(f'unique trips: {len(df.trip_instance_key.unique())}')\n",
+    "            except:\n",
+    "                path = f'{gcs_dir}{table}_{date}/'\n",
+    "                ddf = dd.read_parquet(path)\n",
+    "                print(path)\n",
+    "                print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\n",
+    "                print(f'length: {ddf.shape[0].compute()}')\n",
+    "                print(f'unique trips: {len(ddf.trip_instance_key.unique().compute())}')\n",
+    "                print(ddf.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "81a221c1-7797-48ff-ad26-55acf5f90eec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dates = (shared_utils.rt_dates.DATES['dec2024'],\n",
+    "        shared_utils.rt_dates.DATES['jan2025']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8cf736da-4380-49bf-bcd2-db845b81e6a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['vp', 'vp_usable', 'vp_usable_dwell', 'condensed/vp_condensed']"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f722eb8a-6d6f-4b33-8b85-5d008f2f682a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/vp_2024-12-11.parquet\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 15204207 entries, 0 to 15204206\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                     Dtype              \n",
+      "---  ------                     -----              \n",
+      " 0   gtfs_dataset_name          object             \n",
+      " 1   schedule_gtfs_dataset_key  object             \n",
+      " 2   trip_id                    object             \n",
+      " 3   trip_instance_key          object             \n",
+      " 4   location_timestamp         datetime64[ns, UTC]\n",
+      " 5   location_timestamp_local   datetime64[ns]     \n",
+      " 6   gtfs_dataset_key           category           \n",
+      " 7   geometry                   object             \n",
+      "dtypes: category(1), datetime64[ns, UTC](1), datetime64[ns](1), object(5)\n",
+      "memory usage: 826.5+ MB\n",
+      "None\n",
+      "unique trips: 93389\n",
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/vp_2025-01-15.parquet\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 15516326 entries, 0 to 15516325\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                     Dtype              \n",
+      "---  ------                     -----              \n",
+      " 0   gtfs_dataset_name          object             \n",
+      " 1   schedule_gtfs_dataset_key  object             \n",
+      " 2   trip_id                    object             \n",
+      " 3   trip_instance_key          object             \n",
+      " 4   location_timestamp         datetime64[ns, UTC]\n",
+      " 5   location_timestamp_local   datetime64[ns]     \n",
+      " 6   gtfs_dataset_key           category           \n",
+      " 7   geometry                   object             \n",
+      "dtypes: category(1), datetime64[ns, UTC](1), datetime64[ns](1), object(5)\n",
+      "memory usage: 843.5+ MB\n",
+      "None\n",
+      "unique trips: 93802\n",
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/vp_usable_2024-12-11/\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "length: 15146974\n",
+      "unique trips: 90707\n",
+      "<class 'dask.dataframe.core.DataFrame'>\n",
+      "Columns: 11 entries, gtfs_dataset_name to y\n",
+      "dtypes: category(1), datetime64[ns, UTC](1), datetime64[ns](1), object(5), float64(2), int64(1)None\n",
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/vp_usable_2025-01-15/\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "length: 15452403\n",
+      "unique trips: 90910\n",
+      "<class 'dask.dataframe.core.DataFrame'>\n",
+      "Columns: 11 entries, gtfs_dataset_name to y\n",
+      "dtypes: category(1), datetime64[ns, UTC](1), datetime64[ns](1), object(5), float64(2), int64(1)None\n",
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/vp_usable_dwell_2024-12-11/\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "length: 12555541\n",
+      "unique trips: 90707\n",
+      "<class 'dask.dataframe.core.DataFrame'>\n",
+      "Columns: 16 entries, gtfs_dataset_name to dwell_sec\n",
+      "dtypes: category(1), datetime64[ns, UTC](1), datetime64[ns](2), object(5), float64(2), int64(5)None\n",
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/vp_usable_dwell_2025-01-15/\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "length: 12641977\n",
+      "unique trips: 90910\n",
+      "<class 'dask.dataframe.core.DataFrame'>\n",
+      "Columns: 16 entries, gtfs_dataset_name to dwell_sec\n",
+      "dtypes: category(1), datetime64[ns, UTC](1), datetime64[ns](2), object(5), float64(2), int64(5)None\n",
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/condensed/vp_condensed_2024-12-11.parquet\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 90707 entries, 0 to 90706\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column                    Non-Null Count  Dtype \n",
+      "---  ------                    --------------  ----- \n",
+      " 0   trip_instance_key         90707 non-null  object\n",
+      " 1   geometry                  90707 non-null  object\n",
+      " 2   vp_idx                    90707 non-null  object\n",
+      " 3   location_timestamp_local  90707 non-null  object\n",
+      " 4   moving_timestamp_local    90707 non-null  object\n",
+      " 5   vp_primary_direction      90707 non-null  object\n",
+      "dtypes: object(6)\n",
+      "memory usage: 4.2+ MB\n",
+      "None\n",
+      "unique trips: 90707\n",
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/condensed/vp_condensed_2025-01-15.parquet\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 90910 entries, 0 to 90909\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column                    Non-Null Count  Dtype \n",
+      "---  ------                    --------------  ----- \n",
+      " 0   trip_instance_key         90910 non-null  object\n",
+      " 1   geometry                  90910 non-null  object\n",
+      " 2   vp_idx                    90910 non-null  object\n",
+      " 3   location_timestamp_local  90910 non-null  object\n",
+      " 4   moving_timestamp_local    90910 non-null  object\n",
+      " 5   vp_primary_direction      90910 non-null  object\n",
+      "dtypes: object(6)\n",
+      "memory usage: 4.2+ MB\n",
+      "None\n",
+      "unique trips: 90910\n"
+     ]
+    }
+   ],
+   "source": [
+    "diff_tables(paths=paths, dates=dates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "573f80e1-fc06-4f29-8b05-849da0680db8",
+   "metadata": {},
+   "source": [
+    "## compare stage4 (trip speeds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f1fde5f6-ba02-4c90-836e-649e19de684d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'gs://calitp-analytics-data/data-analyses/rt_segment_speeds/'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "catalog.stop_segments.dir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8e9bc2a1-abef-4866-9cb4-bb4e93a8bede",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'speeds_stop_segments'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "catalog.stop_segments.stage4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "430430b0-fd8a-45a9-9f17-3f0f23ff80b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/nearest/nearest_vp_shape_segments_2024-12-11.parquet\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 3120555 entries, 0 to 3120554\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column             Dtype  \n",
+      "---  ------             -----  \n",
+      " 0   trip_instance_key  object \n",
+      " 1   stop_sequence      int64  \n",
+      " 2   shape_array_key    object \n",
+      " 3   stop_meters        float64\n",
+      " 4   prior_vp_idx       int64  \n",
+      " 5   subseq_vp_idx      int64  \n",
+      " 6   prior_vp_meters    int64  \n",
+      " 7   subseq_vp_meters   float64\n",
+      "dtypes: float64(2), int64(4), object(2)\n",
+      "memory usage: 214.3+ MB\n",
+      "None\n",
+      "unique trips: 79652\n",
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/nearest/nearest_vp_shape_segments_2025-01-15.parquet\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 235003 entries, 0 to 235002\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column             Non-Null Count   Dtype  \n",
+      "---  ------             --------------   -----  \n",
+      " 0   trip_instance_key  235003 non-null  object \n",
+      " 1   stop_sequence      235003 non-null  int64  \n",
+      " 2   shape_array_key    235003 non-null  object \n",
+      " 3   stop_meters        235003 non-null  float64\n",
+      " 4   prior_vp_idx       235003 non-null  int64  \n",
+      " 5   subseq_vp_idx      235003 non-null  int64  \n",
+      " 6   prior_vp_meters    235003 non-null  float64\n",
+      " 7   subseq_vp_meters   235003 non-null  int64  \n",
+      "dtypes: float64(2), int64(4), object(2)\n",
+      "memory usage: 16.1+ MB\n",
+      "None\n",
+      "unique trips: 72366\n"
+     ]
+    }
+   ],
+   "source": [
+    "diff_tables([catalog.stop_segments.stage2], dates, catalog.stop_segments.dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f12b6af2-953d-4c7b-8770-3cf880e96b88",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/nearest/nearest_vp_speedmap_proxy_2024-12-11.parquet\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 577121 entries, 0 to 577120\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column             Non-Null Count   Dtype  \n",
+      "---  ------             --------------   -----  \n",
+      " 0   trip_instance_key  577121 non-null  object \n",
+      " 1   stop_sequence      577121 non-null  int64  \n",
+      " 2   stop_sequence1     577121 non-null  float64\n",
+      " 3   shape_array_key    577121 non-null  object \n",
+      " 4   stop_meters        577121 non-null  float64\n",
+      " 5   prior_vp_idx       577121 non-null  int64  \n",
+      " 6   subseq_vp_idx      577121 non-null  int64  \n",
+      " 7   prior_vp_meters    577121 non-null  int64  \n",
+      " 8   subseq_vp_meters   577121 non-null  float64\n",
+      "dtypes: float64(3), int64(4), object(2)\n",
+      "memory usage: 44.0+ MB\n",
+      "None\n",
+      "unique trips: 51468\n",
+      "gs://calitp-analytics-data/data-analyses/rt_segment_speeds/nearest/nearest_vp_speedmap_proxy_2025-01-15.parquet\n",
+      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 604807 entries, 0 to 604806\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column             Non-Null Count   Dtype  \n",
+      "---  ------             --------------   -----  \n",
+      " 0   trip_instance_key  604807 non-null  object \n",
+      " 1   stop_sequence      604807 non-null  int64  \n",
+      " 2   stop_sequence1     604807 non-null  float64\n",
+      " 3   shape_array_key    604807 non-null  object \n",
+      " 4   stop_meters        604807 non-null  float64\n",
+      " 5   prior_vp_idx       604807 non-null  int64  \n",
+      " 6   subseq_vp_idx      604807 non-null  int64  \n",
+      " 7   prior_vp_meters    604807 non-null  float64\n",
+      " 8   subseq_vp_meters   604807 non-null  float64\n",
+      "dtypes: float64(4), int64(3), object(2)\n",
+      "memory usage: 46.1+ MB\n",
+      "None\n",
+      "unique trips: 51487\n"
+     ]
+    }
+   ],
+   "source": [
+    "diff_tables([catalog.speedmap_segments.stage2], dates, catalog.speedmap_segments.dir)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/rt_segment_speeds/48_investigate_further.ipynb b/rt_segment_speeds/48_investigate_further.ipynb
new file mode 100644
index 000000000..a1453231f
--- /dev/null
+++ b/rt_segment_speeds/48_investigate_further.ipynb
@@ -0,0 +1,2372 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d528f399-cf33-459e-b5dc-92e0b4ae389a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "from siuba import *\n",
+    "import shared_utils\n",
+    "\n",
+    "from calitp_data_analysis import get_fs, geography_utils\n",
+    "from segment_speed_utils import helpers, time_series_utils, gtfs_schedule_wrangling\n",
+    "from segment_speed_utils.project_vars import SCHED_GCS, SEGMENT_GCS, GTFS_DATA_DICT, analysis_date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "42a16a99-113f-48e9-9f8e-f5cea66952ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dask.dataframe as dd\n",
+    "import dask_geopandas as dg\n",
+    "from dask import delayed, compute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83bf87cf-a9f5-4acd-afa6-d0d8920f4a73",
+   "metadata": {},
+   "source": [
+    "# Further investigation after 45_diff_tables\n",
+    "\n",
+    "* https://github.com/cal-itp/data-analyses/pull/1356\n",
+    "* average_segment_speeds looks OK\n",
+    "* issue upstream, specifically with differing segment definitions (stop_pair) supplied via https://github.com/cal-itp/data-analyses/blob/5e83acbafe8312415f1752f25083688561dd42a6/gtfs_funnel/stop_times_with_direction.py vs. those from `gtfs_segments`\n",
+    "* "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6cd318e5-5c4e-4249-b158-232f4d940770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "catalog = shared_utils.catalog_utils.get_catalog('gtfs_analytics_data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "87d3822b-e000-40c8-99a9-8b88bb50364d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('./scripts')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f393cf1d-bf99-4d95-8df3-a351e6e07137",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import average_segment_speeds as aseg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ed2065bd-6b66-4623-92ca-5409ac798f65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from segment_speed_utils.project_vars import analysis_date_list, oct2024_week"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5369fb08-a16d-4bc0-9230-a47f0ce81272",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from shared_utils import time_helpers, dask_utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e74bca08-b455-4139-a5c7-56b85e41c250",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nearest_vp_to_stop as nvp\n",
+    "import pipeline_segment_speeds as ps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4c663e1-dc62-4c7f-815a-e48e52736b18",
+   "metadata": {},
+   "source": [
+    "## explode steps in: [nearest_vp_to_stop](https://github.com/cal-itp/data-analyses/blob/5e83acbafe8312415f1752f25083688561dd42a6/rt_segment_speeds/scripts/nearest_vp_to_stop.py#L20)\n",
+    "\n",
+    "Also compare with December."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a6c22411-fd51-4214-a2b1-da6bce206cbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "segment_type = \"stop_segments\"\n",
+    "# segment_type = \"speedmap_segments\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f5f1f2f7-efee-4091-b14d-7e84bb16cfd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SEGMENT_FILE = GTFS_DATA_DICT[segment_type]['segments_file']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "763e56ed-a8bd-42f0-826c-61e269095e4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_date_dec = shared_utils.rt_dates.DATES['dec2024']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9a31e7f3-1d94-49de-b72f-08bf145ac5ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rt_trips = helpers.import_unique_vp_trips(analysis_date)\n",
+    "rt_trips2 = helpers.import_unique_vp_trips(analysis_date_dec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ef8fb6e2-be9a-4e40-a785-c34a6e782119",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shape_stop_combinations = pd.read_parquet(\n",
+    "        f\"{SEGMENT_GCS}{SEGMENT_FILE}_{analysis_date}.parquet\",\n",
+    "            columns = [\"trip_instance_key\",\n",
+    "                   \"stop_id1\", \"stop_pair\",\n",
+    "                   \"st_trip_instance_key\"],\n",
+    "filters = [[(\"trip_instance_key\", \"in\", rt_trips)]]).rename(columns = {\"stop_id1\": \"stop_id\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9b68691a-6962-4093-a7da-d0440f2cc3c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shape_stop_combinations2 = pd.read_parquet(\n",
+    "        f\"{SEGMENT_GCS}{SEGMENT_FILE}_{shared_utils.rt_dates.DATES['dec2024']}.parquet\",\n",
+    "            columns = [\"trip_instance_key\",\n",
+    "                   \"stop_id1\", \"stop_pair\",\n",
+    "                   \"st_trip_instance_key\"],\n",
+    "filters = [[(\"trip_instance_key\", \"in\", rt_trips2)]]).rename(columns = {\"stop_id1\": \"stop_id\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e1ef784d-156f-4466-9215-002c9dced420",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 3151412 entries, 0 to 3151411\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column                Dtype \n",
+      "---  ------                ----- \n",
+      " 0   trip_instance_key     object\n",
+      " 1   stop_id               object\n",
+      " 2   stop_pair             object\n",
+      " 3   st_trip_instance_key  object\n",
+      "dtypes: object(4)\n",
+      "memory usage: 120.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "shape_stop_combinations.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6ea2ac1b-e3fa-43ae-998e-ae916c877917",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 3150548 entries, 0 to 3150652\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column                Dtype \n",
+      "---  ------                ----- \n",
+      " 0   trip_instance_key     object\n",
+      " 1   stop_id               object\n",
+      " 2   stop_pair             object\n",
+      " 3   st_trip_instance_key  object\n",
+      "dtypes: object(4)\n",
+      "memory usage: 120.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "shape_stop_combinations2.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0692ff77-fee2-402c-abd8-f428f9d3d6fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset_trips2 = shape_stop_combinations2.st_trip_instance_key.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b3074fcb-b597-43b2-8642-366fde4a2af4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset_trips = shape_stop_combinations.st_trip_instance_key.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a8ee02be-4725-401f-95c6-0a66f7b36c16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  GTFS_DATA_DICT.rt_vs_schedule_tables.stop_times_direction\n",
+    "#  written in gtfs_funnel/stop_times_with_direction.py\n",
+    "stops_to_use = helpers.import_scheduled_stop_times(\n",
+    "    analysis_date,\n",
+    "    columns = [\"trip_instance_key\", \"shape_array_key\",\n",
+    "               \"stop_sequence\", \"stop_id\", \"stop_pair\",\n",
+    "               \"stop_primary_direction\", \"geometry\"],\n",
+    "    filters = [[(\"trip_instance_key\", \"in\", subset_trips)]],\n",
+    "    get_pandas = True,\n",
+    "    with_direction = True\n",
+    ").rename(columns = {\"trip_instance_key\": \"st_trip_instance_key\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d78bc54d-0441-4181-aaf5-d4a588326c69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>st_trip_instance_key</th>\n",
+       "      <th>shape_array_key</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_pair</th>\n",
+       "      <th>stop_primary_direction</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>88878</td>\n",
+       "      <td>88878__60308</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>POINT (275996.236 -600448.777)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>60308</td>\n",
+       "      <td>60308__60310</td>\n",
+       "      <td>Eastbound</td>\n",
+       "      <td>POINT (276129.555 -600498.971)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>60309</td>\n",
+       "      <td>60309__60249</td>\n",
+       "      <td>Northbound</td>\n",
+       "      <td>POINT (276315.087 -600745.139)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               st_trip_instance_key                   shape_array_key  \\\n",
+       "0  00001f273308970887743ce0c3acf068  5abad00eed316250607a32ab7aa38fa7   \n",
+       "1  00001f273308970887743ce0c3acf068  5abad00eed316250607a32ab7aa38fa7   \n",
+       "2  00001f273308970887743ce0c3acf068  5abad00eed316250607a32ab7aa38fa7   \n",
+       "\n",
+       "   stop_sequence stop_id     stop_pair stop_primary_direction  \\\n",
+       "0              1   88878  88878__60308                Unknown   \n",
+       "1              2   60308  60308__60310              Eastbound   \n",
+       "2              3   60309  60309__60249             Northbound   \n",
+       "\n",
+       "                         geometry  \n",
+       "0  POINT (275996.236 -600448.777)  \n",
+       "1  POINT (276129.555 -600498.971)  \n",
+       "2  POINT (276315.087 -600745.139)  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stops_to_use.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d86add72-0c08-4262-a1be-d8ec0da936de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_pair</th>\n",
+       "      <th>st_trip_instance_key</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>88878</td>\n",
+       "      <td>88878__60308</td>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>60308</td>\n",
+       "      <td>60308__60309</td>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>60309</td>\n",
+       "      <td>60309__60310</td>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  trip_instance_key stop_id     stop_pair  \\\n",
+       "0  00001f273308970887743ce0c3acf068   88878  88878__60308   \n",
+       "1  00001f273308970887743ce0c3acf068   60308  60308__60309   \n",
+       "2  00001f273308970887743ce0c3acf068   60309  60309__60310   \n",
+       "\n",
+       "               st_trip_instance_key  \n",
+       "0  00001f273308970887743ce0c3acf068  \n",
+       "1  00001f273308970887743ce0c3acf068  \n",
+       "2  00001f273308970887743ce0c3acf068  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape_stop_combinations.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e13c5f90-7c3e-4ffa-b85f-6da54416e1fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>route_short_name</th>\n",
+       "      <th>name</th>\n",
+       "      <th>feed_key</th>\n",
+       "      <th>trip_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>33114</th>\n",
+       "      <td>ffd7d651605dbca64e7b3f46945f5817</td>\n",
+       "      <td>194</td>\n",
+       "      <td>Foothill Schedule</td>\n",
+       "      <td>e920f063b73c35605bb16e98f63d2e6f</td>\n",
+       "      <td>t90B-b2812-sl5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      trip_instance_key route_short_name               name  \\\n",
+       "33114  ffd7d651605dbca64e7b3f46945f5817              194  Foothill Schedule   \n",
+       "\n",
+       "                               feed_key         trip_id  \n",
+       "33114  e920f063b73c35605bb16e98f63d2e6f  t90B-b2812-sl5  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trips.query('trip_instance_key == \"ffd7d651605dbca64e7b3f46945f5817\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "58dd10a4-86fc-482d-8c04-d9e5441c4eca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "left_merge = pd.merge(\n",
+    "    stops_to_use,\n",
+    "    shape_stop_combinations,\n",
+    "    on = [\"st_trip_instance_key\", \"stop_id\", \"stop_pair\"],\n",
+    "    how = \"left\",\n",
+    "    indicator=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d951ba50-3fab-4f24-8542-baa32fe204e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "both          235026\n",
+       "left_only     155801\n",
+       "right_only         0\n",
+       "Name: _merge, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "left_merge._merge.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "60452325-4b1a-4ada-bc1b-ea7b0471698d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "st_trip_instance_key                    ffd7d651605dbca64e7b3f46945f5817\n",
+       "shape_array_key                         a094fd659ed5dffd9c4526cbd6df96c2\n",
+       "stop_sequence                                                       2609\n",
+       "stop_id                                                             1648\n",
+       "stop_pair                                                     1648__2877\n",
+       "stop_primary_direction                                        Northbound\n",
+       "geometry                  POINT (199462.02638845713 -439913.87381394114)\n",
+       "trip_instance_key                                                    NaN\n",
+       "_merge                                                         left_only\n",
+       "Name: 390823, dtype: object"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row = left_merge.loc[390823]\n",
+    "row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "925baa69-daa5-492e-938e-81e885e4e566",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_pair</th>\n",
+       "      <th>st_trip_instance_key</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3151409</th>\n",
+       "      <td>ffd7d651605dbca64e7b3f46945f5817</td>\n",
+       "      <td>1648</td>\n",
+       "      <td>1648__2544</td>\n",
+       "      <td>ffd7d651605dbca64e7b3f46945f5817</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        trip_instance_key stop_id   stop_pair  \\\n",
+       "3151409  ffd7d651605dbca64e7b3f46945f5817    1648  1648__2544   \n",
+       "\n",
+       "                     st_trip_instance_key  \n",
+       "3151409  ffd7d651605dbca64e7b3f46945f5817  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape_stop_combinations.query('st_trip_instance_key == @row.st_trip_instance_key & stop_id == @row.stop_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "2a2ae364-8337-4bc2-9924-01636c323db8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf1 = stops_to_use.query('st_trip_instance_key == @row.st_trip_instance_key & stop_id == @row.stop_id')\n",
+    "\n",
+    "# map pair from shape_stop_combinations_combinations, only using stops_to_use for geom\n",
+    "gdf2 = stops_to_use.query('st_trip_instance_key == @row.st_trip_instance_key & stop_id == \"2544\"')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9542072-9bbf-4b70-ad7b-16801c8fcc10",
+   "metadata": {},
+   "source": [
+    "correct, consecutive stops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "053ace16-8eb0-4260-b39b-c464be6c1077",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7bd3d03952e0>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([gdf1, gdf2]).explore()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "12fe3e45-d612-4812-94ed-3002cc06bb6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf1 = stops_to_use.query('st_trip_instance_key == @row.st_trip_instance_key & stop_id == @row.stop_id')\n",
+    "#  from stops_to_use via helpers.import_scheduled_stop_times\n",
+    "#  GTFS_DATA_DICT.rt_vs_schedule_tables.stop_times_direction\n",
+    "#  written in gtfs_funnel/stop_times_with_direction.py\n",
+    "gdf2 = stops_to_use.query('st_trip_instance_key == @row.st_trip_instance_key & stop_id == \"2877\"')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76f0b7b9-2394-4731-a95e-2662f5e5b712",
+   "metadata": {},
+   "source": [
+    "very far apart, skipping 20+ stops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "f153a325-729b-457f-bf51-b0bb4a361603",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7bd36e52e9a0>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([gdf1, gdf2]).explore()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "8d4f2550-8632-40a9-9c60-a2efaed5c09f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trips = helpers.import_scheduled_trips(analysis_date, columns=['trip_instance_key', 'route_short_name', 'name',\n",
+    "                                                              'feed_key', 'trip_id'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b698e74-19c7-4421-8d2f-37acf037905d",
+   "metadata": {},
+   "source": [
+    "### Another comparison\n",
+    "\n",
+    "referrring back to plain stop_times, the stop_pairs from shape_stop_combinations are in sequence while those from stops_to_use again skip many stops. It seems like the first part of stop_pair there is correct, but the second part is not actually the subsequent stop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "f3f912c2-474a-4384-9530-e31ebe5f4806",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>route_short_name</th>\n",
+       "      <th>name</th>\n",
+       "      <th>feed_key</th>\n",
+       "      <th>trip_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>36933</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>169</td>\n",
+       "      <td>LA Metro Bus Schedule</td>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      trip_instance_key route_short_name  \\\n",
+       "36933  0c9b170807546736de0820cf8860bc00              169   \n",
+       "\n",
+       "                        name                          feed_key  \\\n",
+       "36933  LA Metro Bus Schedule  7a5a0fa93eb3c7a402c0887a4f11f066   \n",
+       "\n",
+       "                    trip_id  \n",
+       "36933  10169001531434-DEC24  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trips.query('trip_instance_key == \"0c9b170807546736de0820cf8860bc00\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "24a2ee38-5e50-452f-bab8-406fdfbcfb90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_pair</th>\n",
+       "      <th>st_trip_instance_key</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2432353</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>30021</td>\n",
+       "      <td>30021__3625</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2432354</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>3625</td>\n",
+       "      <td>3625__12116</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2432355</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>12116</td>\n",
+       "      <td>12116__2499</td>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        trip_instance_key stop_id    stop_pair  \\\n",
+       "2432353  0c9b170807546736de0820cf8860bc00   30021  30021__3625   \n",
+       "2432354  0c9b170807546736de0820cf8860bc00    3625  3625__12116   \n",
+       "2432355  0c9b170807546736de0820cf8860bc00   12116  12116__2499   \n",
+       "\n",
+       "                     st_trip_instance_key  \n",
+       "2432353  0c9b170807546736de0820cf8860bc00  \n",
+       "2432354  0c9b170807546736de0820cf8860bc00  \n",
+       "2432355  0c9b170807546736de0820cf8860bc00  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape_stop_combinations.query('trip_instance_key == \"0c9b170807546736de0820cf8860bc00\"').head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "416c311e-d4d3-4f0f-9384-3befe2e6e71c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>st_trip_instance_key</th>\n",
+       "      <th>shape_array_key</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>stop_id</th>\n",
+       "      <th>stop_pair</th>\n",
+       "      <th>stop_primary_direction</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>69818</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30021</td>\n",
+       "      <td>30021__3325</td>\n",
+       "      <td>Eastbound</td>\n",
+       "      <td>POINT (151920.171 -423388.371)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69819</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3625</td>\n",
+       "      <td>3625__3252</td>\n",
+       "      <td>Eastbound</td>\n",
+       "      <td>POINT (152164.852 -423047.563)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69820</th>\n",
+       "      <td>0c9b170807546736de0820cf8860bc00</td>\n",
+       "      <td>59459dbb9f7ce418d6f88f65e905d583</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12116</td>\n",
+       "      <td>12116__14463</td>\n",
+       "      <td>Eastbound</td>\n",
+       "      <td>POINT (152163.927 -422698.157)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   st_trip_instance_key                   shape_array_key  \\\n",
+       "69818  0c9b170807546736de0820cf8860bc00  59459dbb9f7ce418d6f88f65e905d583   \n",
+       "69819  0c9b170807546736de0820cf8860bc00  59459dbb9f7ce418d6f88f65e905d583   \n",
+       "69820  0c9b170807546736de0820cf8860bc00  59459dbb9f7ce418d6f88f65e905d583   \n",
+       "\n",
+       "       stop_sequence stop_id     stop_pair stop_primary_direction  \\\n",
+       "69818              1   30021   30021__3325              Eastbound   \n",
+       "69819              2    3625    3625__3252              Eastbound   \n",
+       "69820              3   12116  12116__14463              Eastbound   \n",
+       "\n",
+       "                             geometry  \n",
+       "69818  POINT (151920.171 -423388.371)  \n",
+       "69819  POINT (152164.852 -423047.563)  \n",
+       "69820  POINT (152163.927 -422698.157)  "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stops_to_use.query('st_trip_instance_key == \"0c9b170807546736de0820cf8860bc00\"').head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "114d69ac-6ca8-43c9-8271-b722edf2aa27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st_no_dir = helpers.import_scheduled_stop_times(analysis_date, with_direction=False, get_pandas=True,\n",
+    "                                               columns=['feed_key', 'trip_id', 'stop_sequence',\n",
+    "                                                       'stop_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "07329da2-611a-4ffc-81de-cd84837b1bce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feed_key</th>\n",
+       "      <th>trip_id</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>stop_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2448922</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1766211</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3255026</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279772</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>779733</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1278824</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>130</td>\n",
+       "      <td>6575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2925060</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>131</td>\n",
+       "      <td>6597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2753655</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>132</td>\n",
+       "      <td>7148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1436805</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>133</td>\n",
+       "      <td>5507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1623189</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>134</td>\n",
+       "      <td>5505</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>134 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 feed_key               trip_id  \\\n",
+       "2448922  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "1766211  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "3255026  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "1279772  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "779733   7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "...                                   ...                   ...   \n",
+       "1278824  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "2925060  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "2753655  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "1436805  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "1623189  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "\n",
+       "         stop_sequence stop_id  \n",
+       "2448922              1   30021  \n",
+       "1766211              2    3625  \n",
+       "3255026              3   12116  \n",
+       "1279772              4    2499  \n",
+       "779733               5    3567  \n",
+       "...                ...     ...  \n",
+       "1278824            130    6575  \n",
+       "2925060            131    6597  \n",
+       "2753655            132    7148  \n",
+       "1436805            133    5507  \n",
+       "1623189            134    5505  \n",
+       "\n",
+       "[134 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st_no_dir.query('feed_key == \"7a5a0fa93eb3c7a402c0887a4f11f066\" & trip_id == \"10169001531434-DEC24\"').sort_values('stop_sequence')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "e44785f7-d8cc-4a84-8290-41f5ad590b4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feed_key</th>\n",
+       "      <th>trip_id</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>stop_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1608478</th>\n",
+       "      <td>7a5a0fa93eb3c7a402c0887a4f11f066</td>\n",
+       "      <td>10169001531434-DEC24</td>\n",
+       "      <td>118</td>\n",
+       "      <td>3325</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 feed_key               trip_id  \\\n",
+       "1608478  7a5a0fa93eb3c7a402c0887a4f11f066  10169001531434-DEC24   \n",
+       "\n",
+       "         stop_sequence stop_id  \n",
+       "1608478            118    3325  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(st_no_dir.query('feed_key == \"7a5a0fa93eb3c7a402c0887a4f11f066\" & trip_id == \"10169001531434-DEC24\"'\n",
+    "                ).sort_values('stop_sequence'\n",
+    "                ).query('stop_id == \"3325\"')\n",
+    "\n",
+    ")\n",
+    "#  stop_sequence 118 but expect 2!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09acfafa-fa1e-4137-ad76-3d4a893743c2",
+   "metadata": {},
+   "source": [
+    "### December data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "3ce7796d-d15d-4257-9d80-d1021e5211d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stops_to_use2 = helpers.import_scheduled_stop_times(\n",
+    "    analysis_date_dec,\n",
+    "    columns = [\"trip_instance_key\", \"shape_array_key\",\n",
+    "               \"stop_sequence\", \"stop_id\", \"stop_pair\",\n",
+    "               \"stop_primary_direction\", \"geometry\"],\n",
+    "    filters = [[(\"trip_instance_key\", \"in\", subset_trips2)]],\n",
+    "    get_pandas = True,\n",
+    "    with_direction = True\n",
+    ").rename(columns = {\"trip_instance_key\": \"st_trip_instance_key\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "7319e8b3-2ba6-47b9-a552-c635d8ce4a2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "left_merge2 = pd.merge(\n",
+    "    stops_to_use2,\n",
+    "    shape_stop_combinations2,\n",
+    "    on = [\"st_trip_instance_key\", \"stop_id\", \"stop_pair\"],\n",
+    "    how = \"left\",\n",
+    "    indicator=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ca6140c6-5574-4c1c-9034-c6e12722b16c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "both          3134260\n",
+       "left_only        5232\n",
+       "right_only          0\n",
+       "Name: _merge, dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "left_merge2._merge.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4231d5de-2fbf-47f8-88cb-7648c3186e79",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "# Averaging seems to work OK actually, problem in previous step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "1b82a625-85a5-47d0-b7c5-4ef19d70139e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dict_inputs = catalog[\"stop_segments\"]\n",
+    "shape_stop_combinations_COLS = [*dict_inputs[\"shape_stop_combinations_cols\"]]\n",
+    "ROUTE_DIR_COLS = [*dict_inputs[\"route_dir_cols\"]]\n",
+    "STOP_PAIR_COLS = [*dict_inputs[\"stop_pair_cols\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "1f3a3b54-4e78-4e1c-b82b-afe6b56a76f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SPEED_FILE = dict_inputs[\"stage4\"]\n",
+    "MAX_SPEED = dict_inputs[\"max_speed\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "e20de26d-facd-4a55-91be-58655308cc17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2025-01-15']"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "analysis_date_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "b6c7206e-e2e8-490d-9979-3ba08f12c483",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'speeds_stop_segments'"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SPEED_FILE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "aa1ef396-7508-4852-adf6-f48f1f889a54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = 'gs://calitp-analytics-data/data-analyses/rt_segment_speeds/speedmap/speeds_2025-01-15.parquet'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "fa747a53-ab29-47e0-aab6-9f0b45b81450",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_parquet(\n",
+    "            path,\n",
+    "            # **kwargs,\n",
+    "        ).drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "8cff9d11-716d-4ba2-b4d1-4e28db42cdc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = 'gs://calitp-analytics-data/data-analyses/rt_segment_speeds/speedmap/speeds'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "fbff6d6b-94c7-4d6a-83e9-9cf8f869c350",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2025-01-15']"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "analysis_date_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "3f10f9f4-9046-4b12-a086-2034b1e1bdf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ddf=dask_utils.get_ddf([path], analysis_date_list, 'df', get_pandas = False, add_date = True) #  this is fine\n",
+    "ddf=dask_utils.get_ddf([path], analysis_date_list, 'df', get_pandas = False,\n",
+    "                       add_date = True, columns = cols, filters = [[(\"speed_mph\", \"<=\", MAX_SPEED)]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "2a22ab6e-fe10-48c3-a2e9-1ef44aa249e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>schedule_gtfs_dataset_key</th>\n",
+       "      <th>shape_array_key</th>\n",
+       "      <th>shape_id</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>route_id</th>\n",
+       "      <th>direction_id</th>\n",
+       "      <th>stop_pair</th>\n",
+       "      <th>stop_pair_name</th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>speed_mph</th>\n",
+       "      <th>meters_elapsed</th>\n",
+       "      <th>sec_elapsed</th>\n",
+       "      <th>time_of_day</th>\n",
+       "      <th>arrival_time</th>\n",
+       "      <th>service_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>2</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60308__60310</td>\n",
+       "      <td>Beyer Bl &amp; Iris Av__Beyer Bl &amp; Precision Park Ln</td>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>3.443793</td>\n",
+       "      <td>307.893917</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 18:09:27</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>3</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60309__60249</td>\n",
+       "      <td>Beyer Bl &amp; Dairy Mart Rd__San Ysidro Bl &amp; Virg...</td>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>10.917496</td>\n",
+       "      <td>229.379660</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 18:12:47</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>4</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60310__60568</td>\n",
+       "      <td>Beyer Bl &amp; Precision Park Ln__San Ysidro Bl &amp; ...</td>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>10.915386</td>\n",
+       "      <td>1039.328261</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 18:13:34</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>6</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60245__60567</td>\n",
+       "      <td>Beyer Bl &amp; Poplar St__San Ysidro Bl &amp; Dairy Ma...</td>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>10.445412</td>\n",
+       "      <td>364.211940</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 18:17:07</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>7</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60052__60574</td>\n",
+       "      <td>South Vista Av &amp; Sunset Ln__San Ysidro Bl &amp; Cy...</td>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>14.550872</td>\n",
+       "      <td>136.597364</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 18:18:25</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3074227</th>\n",
+       "      <td>f74424acf8c41e4c1e9fd42838c4875c</td>\n",
+       "      <td>a094fd659ed5dffd9c4526cbd6df96c2</td>\n",
+       "      <td>18468_shp</td>\n",
+       "      <td>2160</td>\n",
+       "      <td>194</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3356__3324</td>\n",
+       "      <td>Valley Blvd and Lemon Ave__Valley Blvd and Max...</td>\n",
+       "      <td>ffd7d651605dbca64e7b3f46945f5817</td>\n",
+       "      <td>36.749597</td>\n",
+       "      <td>1002.112387</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>Evening</td>\n",
+       "      <td>2025-01-15 23:54:48</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3074228</th>\n",
+       "      <td>f74424acf8c41e4c1e9fd42838c4875c</td>\n",
+       "      <td>a094fd659ed5dffd9c4526cbd6df96c2</td>\n",
+       "      <td>18468_shp</td>\n",
+       "      <td>2280</td>\n",
+       "      <td>194</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3324__3337</td>\n",
+       "      <td>Valley Blvd and Pierre Rd E__Old Valley Blvd a...</td>\n",
+       "      <td>ffd7d651605dbca64e7b3f46945f5817</td>\n",
+       "      <td>39.970207</td>\n",
+       "      <td>375.223222</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>Evening</td>\n",
+       "      <td>2025-01-15 23:54:54</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3074229</th>\n",
+       "      <td>f74424acf8c41e4c1e9fd42838c4875c</td>\n",
+       "      <td>a094fd659ed5dffd9c4526cbd6df96c2</td>\n",
+       "      <td>18468_shp</td>\n",
+       "      <td>2322</td>\n",
+       "      <td>194</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3329__3342</td>\n",
+       "      <td>Valley Blvd and Suzanne Rd E__Azusa Way and Va...</td>\n",
+       "      <td>ffd7d651605dbca64e7b3f46945f5817</td>\n",
+       "      <td>23.061093</td>\n",
+       "      <td>639.154132</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>Evening</td>\n",
+       "      <td>2025-01-15 23:55:15</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3074230</th>\n",
+       "      <td>f74424acf8c41e4c1e9fd42838c4875c</td>\n",
+       "      <td>a094fd659ed5dffd9c4526cbd6df96c2</td>\n",
+       "      <td>18468_shp</td>\n",
+       "      <td>2393</td>\n",
+       "      <td>194</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3319__3340</td>\n",
+       "      <td>Valley Blvd and Brea Canyon Rd E__Mayor Dave W...</td>\n",
+       "      <td>ffd7d651605dbca64e7b3f46945f5817</td>\n",
+       "      <td>25.971102</td>\n",
+       "      <td>940.393061</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>Evening</td>\n",
+       "      <td>2025-01-15 23:56:17</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3074231</th>\n",
+       "      <td>f74424acf8c41e4c1e9fd42838c4875c</td>\n",
+       "      <td>a094fd659ed5dffd9c4526cbd6df96c2</td>\n",
+       "      <td>18468_shp</td>\n",
+       "      <td>2498</td>\n",
+       "      <td>194</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3371__3316</td>\n",
+       "      <td>Grand Ave and Valley Blvd__Central Ave and 2nd St</td>\n",
+       "      <td>ffd7d651605dbca64e7b3f46945f5817</td>\n",
+       "      <td>36.815938</td>\n",
+       "      <td>987.463706</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>Evening</td>\n",
+       "      <td>2025-01-15 23:57:38</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2897310 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                schedule_gtfs_dataset_key                   shape_array_key  \\\n",
+       "0        baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "1        baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "2        baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "3        baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "4        baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "...                                   ...                               ...   \n",
+       "3074227  f74424acf8c41e4c1e9fd42838c4875c  a094fd659ed5dffd9c4526cbd6df96c2   \n",
+       "3074228  f74424acf8c41e4c1e9fd42838c4875c  a094fd659ed5dffd9c4526cbd6df96c2   \n",
+       "3074229  f74424acf8c41e4c1e9fd42838c4875c  a094fd659ed5dffd9c4526cbd6df96c2   \n",
+       "3074230  f74424acf8c41e4c1e9fd42838c4875c  a094fd659ed5dffd9c4526cbd6df96c2   \n",
+       "3074231  f74424acf8c41e4c1e9fd42838c4875c  a094fd659ed5dffd9c4526cbd6df96c2   \n",
+       "\n",
+       "          shape_id  stop_sequence route_id  direction_id     stop_pair  \\\n",
+       "0         906_8_38              2      906           0.0  60308__60310   \n",
+       "1         906_8_38              3      906           0.0  60309__60249   \n",
+       "2         906_8_38              4      906           0.0  60310__60568   \n",
+       "3         906_8_38              6      906           0.0  60245__60567   \n",
+       "4         906_8_38              7      906           0.0  60052__60574   \n",
+       "...            ...            ...      ...           ...           ...   \n",
+       "3074227  18468_shp           2160      194           0.0    3356__3324   \n",
+       "3074228  18468_shp           2280      194           0.0    3324__3337   \n",
+       "3074229  18468_shp           2322      194           0.0    3329__3342   \n",
+       "3074230  18468_shp           2393      194           0.0    3319__3340   \n",
+       "3074231  18468_shp           2498      194           0.0    3371__3316   \n",
+       "\n",
+       "                                            stop_pair_name  \\\n",
+       "0         Beyer Bl & Iris Av__Beyer Bl & Precision Park Ln   \n",
+       "1        Beyer Bl & Dairy Mart Rd__San Ysidro Bl & Virg...   \n",
+       "2        Beyer Bl & Precision Park Ln__San Ysidro Bl & ...   \n",
+       "3        Beyer Bl & Poplar St__San Ysidro Bl & Dairy Ma...   \n",
+       "4        South Vista Av & Sunset Ln__San Ysidro Bl & Cy...   \n",
+       "...                                                    ...   \n",
+       "3074227  Valley Blvd and Lemon Ave__Valley Blvd and Max...   \n",
+       "3074228  Valley Blvd and Pierre Rd E__Old Valley Blvd a...   \n",
+       "3074229  Valley Blvd and Suzanne Rd E__Azusa Way and Va...   \n",
+       "3074230  Valley Blvd and Brea Canyon Rd E__Mayor Dave W...   \n",
+       "3074231  Grand Ave and Valley Blvd__Central Ave and 2nd St   \n",
+       "\n",
+       "                        trip_instance_key  speed_mph  meters_elapsed  \\\n",
+       "0        00001f273308970887743ce0c3acf068   3.443793      307.893917   \n",
+       "1        00001f273308970887743ce0c3acf068  10.917496      229.379660   \n",
+       "2        00001f273308970887743ce0c3acf068  10.915386     1039.328261   \n",
+       "3        00001f273308970887743ce0c3acf068  10.445412      364.211940   \n",
+       "4        00001f273308970887743ce0c3acf068  14.550872      136.597364   \n",
+       "...                                   ...        ...             ...   \n",
+       "3074227  ffd7d651605dbca64e7b3f46945f5817  36.749597     1002.112387   \n",
+       "3074228  ffd7d651605dbca64e7b3f46945f5817  39.970207      375.223222   \n",
+       "3074229  ffd7d651605dbca64e7b3f46945f5817  23.061093      639.154132   \n",
+       "3074230  ffd7d651605dbca64e7b3f46945f5817  25.971102      940.393061   \n",
+       "3074231  ffd7d651605dbca64e7b3f46945f5817  36.815938      987.463706   \n",
+       "\n",
+       "         sec_elapsed time_of_day        arrival_time service_date  \n",
+       "0              200.0     PM Peak 2025-01-15 18:09:27   2025-01-15  \n",
+       "1               47.0     PM Peak 2025-01-15 18:12:47   2025-01-15  \n",
+       "2              213.0     PM Peak 2025-01-15 18:13:34   2025-01-15  \n",
+       "3               78.0     PM Peak 2025-01-15 18:17:07   2025-01-15  \n",
+       "4               21.0     PM Peak 2025-01-15 18:18:25   2025-01-15  \n",
+       "...              ...         ...                 ...          ...  \n",
+       "3074227         61.0     Evening 2025-01-15 23:54:48   2025-01-15  \n",
+       "3074228         21.0     Evening 2025-01-15 23:54:54   2025-01-15  \n",
+       "3074229         62.0     Evening 2025-01-15 23:55:15   2025-01-15  \n",
+       "3074230         81.0     Evening 2025-01-15 23:56:17   2025-01-15  \n",
+       "3074231         60.0     Evening 2025-01-15 23:57:38   2025-01-15  \n",
+       "\n",
+       "[2897310 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "505d5d05-3b24-46f5-8ffe-7f098daa56da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = helpers.unique_list(\n",
+    "        aseg.OPERATOR_COLS + shape_stop_combinations_COLS + \n",
+    "        ROUTE_DIR_COLS + STOP_PAIR_COLS + [\n",
+    "            \"trip_instance_key\", \"speed_mph\", \n",
+    "            \"meters_elapsed\", \"sec_elapsed\", \n",
+    "            \"time_of_day\", \"arrival_time\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "1efe821e-0148-4ff9-8b53-21fd97d455e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['schedule_gtfs_dataset_key',\n",
+       " 'shape_array_key',\n",
+       " 'shape_id',\n",
+       " 'stop_sequence',\n",
+       " 'route_id',\n",
+       " 'direction_id',\n",
+       " 'stop_pair',\n",
+       " 'stop_pair_name',\n",
+       " 'trip_instance_key',\n",
+       " 'speed_mph',\n",
+       " 'meters_elapsed',\n",
+       " 'sec_elapsed',\n",
+       " 'time_of_day',\n",
+       " 'arrival_time']"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "391bb3a8-d110-4b1b-ac00-3c78e02e597b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['trip_instance_key', 'stop_sequence', 'stop_sequence1',\n",
+       "       'shape_array_key', 'stop_meters', 'arrival_time', 'arrival_time_sec',\n",
+       "       'subseq_arrival_time_sec', 'subseq_stop_meters', 'meters_elapsed',\n",
+       "       'sec_elapsed', 'speed_mph', 'schedule_gtfs_dataset_key', 'route_id',\n",
+       "       'direction_id', 'route_short_name', 'scheduled_service_minutes',\n",
+       "       'sched_rt_category', 'time_of_day', 'shape_id', 'segment_id',\n",
+       "       'stop_pair_name', 'stop_pair'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "c02cccde-0885-42ef-8c64-5bf65c4c6ec7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'gs://calitp-analytics-data/data-analyses/rt_segment_speeds/'"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aseg.SEGMENT_GCS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "fe322754-74d1-4976-bee6-099c7518f6d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = time_series_utils.concatenate_datasets_across_dates(\n",
+    "    aseg.SEGMENT_GCS, \n",
+    "    SPEED_FILE,\n",
+    "    analysis_date_list,\n",
+    "    data_type  = \"df\",\n",
+    "    columns = helpers.unique_list(\n",
+    "        aseg.OPERATOR_COLS + SHAPE_STOP_COLS + \n",
+    "        ROUTE_DIR_COLS + STOP_PAIR_COLS + [\n",
+    "            \"trip_instance_key\", \"speed_mph\", \n",
+    "            \"meters_elapsed\", \"sec_elapsed\", \n",
+    "            \"time_of_day\", \"arrival_time\"]),\n",
+    "    filters = [[(\"speed_mph\", \"<=\", MAX_SPEED)]],\n",
+    "    get_pandas=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7402508c-5fee-45d9-9b18-18b085194dea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>schedule_gtfs_dataset_key</th>\n",
+       "      <th>shape_array_key</th>\n",
+       "      <th>shape_id</th>\n",
+       "      <th>stop_sequence</th>\n",
+       "      <th>route_id</th>\n",
+       "      <th>direction_id</th>\n",
+       "      <th>stop_pair</th>\n",
+       "      <th>stop_pair_name</th>\n",
+       "      <th>trip_instance_key</th>\n",
+       "      <th>speed_mph</th>\n",
+       "      <th>meters_elapsed</th>\n",
+       "      <th>sec_elapsed</th>\n",
+       "      <th>time_of_day</th>\n",
+       "      <th>arrival_time</th>\n",
+       "      <th>service_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>12</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60314__60315</td>\n",
+       "      <td>San Ysidro Bl &amp; I-805 (Ramp)__San Ysidro Bl &amp; ...</td>\n",
+       "      <td>00001f273308970887743ce0c3acf068</td>\n",
+       "      <td>5.870617</td>\n",
+       "      <td>3584.829289</td>\n",
+       "      <td>1366.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 18:22:03</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>12</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60314__60315</td>\n",
+       "      <td>San Ysidro Bl &amp; I-805 (Ramp)__San Ysidro Bl &amp; ...</td>\n",
+       "      <td>093275bdc15202d91c8bea881866e7b2</td>\n",
+       "      <td>6.446353</td>\n",
+       "      <td>3584.829289</td>\n",
+       "      <td>1244.0</td>\n",
+       "      <td>Midday</td>\n",
+       "      <td>2025-01-15 13:49:05</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>12</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60314__60315</td>\n",
+       "      <td>San Ysidro Bl &amp; I-805 (Ramp)__San Ysidro Bl &amp; ...</td>\n",
+       "      <td>0b309afa2d5bef3c2028961646c94ba3</td>\n",
+       "      <td>7.077902</td>\n",
+       "      <td>3584.829289</td>\n",
+       "      <td>1133.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 17:09:01</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>12</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60314__60315</td>\n",
+       "      <td>San Ysidro Bl &amp; I-805 (Ramp)__San Ysidro Bl &amp; ...</td>\n",
+       "      <td>0f5769de811a55b20dc44ffa816de412</td>\n",
+       "      <td>5.289751</td>\n",
+       "      <td>3584.829289</td>\n",
+       "      <td>1516.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 16:54:38</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>baeeb157e85a901e47b828ef9fe75091</td>\n",
+       "      <td>5abad00eed316250607a32ab7aa38fa7</td>\n",
+       "      <td>906_8_38</td>\n",
+       "      <td>12</td>\n",
+       "      <td>906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60314__60315</td>\n",
+       "      <td>San Ysidro Bl &amp; I-805 (Ramp)__San Ysidro Bl &amp; ...</td>\n",
+       "      <td>1288107588bb83361fda270fee48bdba</td>\n",
+       "      <td>6.836541</td>\n",
+       "      <td>3584.829289</td>\n",
+       "      <td>1173.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 19:34:48</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191821</th>\n",
+       "      <td>c499f905e33929a641f083dad55c521e</td>\n",
+       "      <td>0227946648df4c7ba490c34bdc6c6ee3</td>\n",
+       "      <td>shp-696-02</td>\n",
+       "      <td>26</td>\n",
+       "      <td>696</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>50127__57896</td>\n",
+       "      <td>Florence Av &amp; Hermosa Av__Florence Av &amp; Broadw...</td>\n",
+       "      <td>fddaf72e40d72fbfea96f069a272451b</td>\n",
+       "      <td>11.322115</td>\n",
+       "      <td>313.800230</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>AM Peak</td>\n",
+       "      <td>2025-01-15 08:33:13</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191822</th>\n",
+       "      <td>c499f905e33929a641f083dad55c521e</td>\n",
+       "      <td>0227946648df4c7ba490c34bdc6c6ee3</td>\n",
+       "      <td>shp-696-02</td>\n",
+       "      <td>27</td>\n",
+       "      <td>696</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>57896__59519</td>\n",
+       "      <td>Florence Av &amp; Broadway Ter__Broadway Ter &amp; Her...</td>\n",
+       "      <td>fddaf72e40d72fbfea96f069a272451b</td>\n",
+       "      <td>14.082307</td>\n",
+       "      <td>2379.576195</td>\n",
+       "      <td>378.0</td>\n",
+       "      <td>AM Peak</td>\n",
+       "      <td>2025-01-15 08:34:15</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191826</th>\n",
+       "      <td>364d59b3aea55aec2962a0b3244a40e0</td>\n",
+       "      <td>24d7ff7f53f1dd1ac665462891afdde6</td>\n",
+       "      <td>162</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>208__210</td>\n",
+       "      <td>Vanderbilt Wy &amp; Westinghouse Pl__Ave Stanford ...</td>\n",
+       "      <td>fe6f2489a8dc41b382f4491539db22c7</td>\n",
+       "      <td>16.131704</td>\n",
+       "      <td>2105.703045</td>\n",
+       "      <td>292.0</td>\n",
+       "      <td>AM Peak</td>\n",
+       "      <td>2025-01-15 09:23:13</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191828</th>\n",
+       "      <td>0666caf3ec1ecc96b74f4477ee4bc939</td>\n",
+       "      <td>f82809b9f53bd208c67890c43aa4dcec</td>\n",
+       "      <td>2420002_DEC24</td>\n",
+       "      <td>12</td>\n",
+       "      <td>242-13186</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8492__8483</td>\n",
+       "      <td>Winnetka / Vanowen__Winnetka / Hart</td>\n",
+       "      <td>fe970eb78c0742910069087708c74666</td>\n",
+       "      <td>10.843088</td>\n",
+       "      <td>9888.197968</td>\n",
+       "      <td>2040.0</td>\n",
+       "      <td>PM Peak</td>\n",
+       "      <td>2025-01-15 16:09:53</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191832</th>\n",
+       "      <td>f74424acf8c41e4c1e9fd42838c4875c</td>\n",
+       "      <td>a094fd659ed5dffd9c4526cbd6df96c2</td>\n",
+       "      <td>18468_shp</td>\n",
+       "      <td>758</td>\n",
+       "      <td>194</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3326__3321</td>\n",
+       "      <td>Valley Blvd and San Angelo Ave__Valley Blvd an...</td>\n",
+       "      <td>ffd7d651605dbca64e7b3f46945f5817</td>\n",
+       "      <td>24.445431</td>\n",
+       "      <td>13725.284327</td>\n",
+       "      <td>1256.0</td>\n",
+       "      <td>Evening</td>\n",
+       "      <td>2025-01-15 23:29:05</td>\n",
+       "      <td>2025-01-15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>122021 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               schedule_gtfs_dataset_key                   shape_array_key  \\\n",
+       "0       baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "1       baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "2       baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "3       baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "4       baeeb157e85a901e47b828ef9fe75091  5abad00eed316250607a32ab7aa38fa7   \n",
+       "...                                  ...                               ...   \n",
+       "191821  c499f905e33929a641f083dad55c521e  0227946648df4c7ba490c34bdc6c6ee3   \n",
+       "191822  c499f905e33929a641f083dad55c521e  0227946648df4c7ba490c34bdc6c6ee3   \n",
+       "191826  364d59b3aea55aec2962a0b3244a40e0  24d7ff7f53f1dd1ac665462891afdde6   \n",
+       "191828  0666caf3ec1ecc96b74f4477ee4bc939  f82809b9f53bd208c67890c43aa4dcec   \n",
+       "191832  f74424acf8c41e4c1e9fd42838c4875c  a094fd659ed5dffd9c4526cbd6df96c2   \n",
+       "\n",
+       "             shape_id  stop_sequence   route_id  direction_id     stop_pair  \\\n",
+       "0            906_8_38             12        906           0.0  60314__60315   \n",
+       "1            906_8_38             12        906           0.0  60314__60315   \n",
+       "2            906_8_38             12        906           0.0  60314__60315   \n",
+       "3            906_8_38             12        906           0.0  60314__60315   \n",
+       "4            906_8_38             12        906           0.0  60314__60315   \n",
+       "...               ...            ...        ...           ...           ...   \n",
+       "191821     shp-696-02             26        696           0.0  50127__57896   \n",
+       "191822     shp-696-02             27        696           0.0  57896__59519   \n",
+       "191826            162             13          1           0.0      208__210   \n",
+       "191828  2420002_DEC24             12  242-13186           0.0    8492__8483   \n",
+       "191832      18468_shp            758        194           0.0    3326__3321   \n",
+       "\n",
+       "                                           stop_pair_name  \\\n",
+       "0       San Ysidro Bl & I-805 (Ramp)__San Ysidro Bl & ...   \n",
+       "1       San Ysidro Bl & I-805 (Ramp)__San Ysidro Bl & ...   \n",
+       "2       San Ysidro Bl & I-805 (Ramp)__San Ysidro Bl & ...   \n",
+       "3       San Ysidro Bl & I-805 (Ramp)__San Ysidro Bl & ...   \n",
+       "4       San Ysidro Bl & I-805 (Ramp)__San Ysidro Bl & ...   \n",
+       "...                                                   ...   \n",
+       "191821  Florence Av & Hermosa Av__Florence Av & Broadw...   \n",
+       "191822  Florence Av & Broadway Ter__Broadway Ter & Her...   \n",
+       "191826  Vanderbilt Wy & Westinghouse Pl__Ave Stanford ...   \n",
+       "191828                Winnetka / Vanowen__Winnetka / Hart   \n",
+       "191832  Valley Blvd and San Angelo Ave__Valley Blvd an...   \n",
+       "\n",
+       "                       trip_instance_key  speed_mph  meters_elapsed  \\\n",
+       "0       00001f273308970887743ce0c3acf068   5.870617     3584.829289   \n",
+       "1       093275bdc15202d91c8bea881866e7b2   6.446353     3584.829289   \n",
+       "2       0b309afa2d5bef3c2028961646c94ba3   7.077902     3584.829289   \n",
+       "3       0f5769de811a55b20dc44ffa816de412   5.289751     3584.829289   \n",
+       "4       1288107588bb83361fda270fee48bdba   6.836541     3584.829289   \n",
+       "...                                  ...        ...             ...   \n",
+       "191821  fddaf72e40d72fbfea96f069a272451b  11.322115      313.800230   \n",
+       "191822  fddaf72e40d72fbfea96f069a272451b  14.082307     2379.576195   \n",
+       "191826  fe6f2489a8dc41b382f4491539db22c7  16.131704     2105.703045   \n",
+       "191828  fe970eb78c0742910069087708c74666  10.843088     9888.197968   \n",
+       "191832  ffd7d651605dbca64e7b3f46945f5817  24.445431    13725.284327   \n",
+       "\n",
+       "        sec_elapsed time_of_day        arrival_time service_date  \n",
+       "0            1366.0     PM Peak 2025-01-15 18:22:03   2025-01-15  \n",
+       "1            1244.0      Midday 2025-01-15 13:49:05   2025-01-15  \n",
+       "2            1133.0     PM Peak 2025-01-15 17:09:01   2025-01-15  \n",
+       "3            1516.0     PM Peak 2025-01-15 16:54:38   2025-01-15  \n",
+       "4            1173.0     PM Peak 2025-01-15 19:34:48   2025-01-15  \n",
+       "...             ...         ...                 ...          ...  \n",
+       "191821         62.0     AM Peak 2025-01-15 08:33:13   2025-01-15  \n",
+       "191822        378.0     AM Peak 2025-01-15 08:34:15   2025-01-15  \n",
+       "191826        292.0     AM Peak 2025-01-15 09:23:13   2025-01-15  \n",
+       "191828       2040.0     PM Peak 2025-01-15 16:09:53   2025-01-15  \n",
+       "191832       1256.0     Evening 2025-01-15 23:29:05   2025-01-15  \n",
+       "\n",
+       "[122021 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ba9c7a7-e198-4c2d-8544-b6a896793ed5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = aseg.concatenate_trip_segment_speeds(\n",
+    "        analysis_date_list,\n",
+    "        \"stop_segments\",\n",
+    "        get_pandas = False\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58fdebe4-2794-402f-a22d-a203c5c9b336",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b903a02c-5c7d-4ef8-bf03-ec8c63cd51ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trip_speeds = df.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e962769-d08a-4c2a-9168-67638049dfd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trip_speeds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39b9d782-d52f-4309-a563-7b21de27dc72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_cols = aseg.OPERATOR_COLS + ROUTE_DIR_COLS + STOP_PAIR_COLS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba487f4a-21d7-4227-a8a0-b80a7cdcec9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a52bec21-d581-417e-9fec-fbabf590ed89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_speeds = delayed(aseg.metrics.concatenate_peak_offpeak_allday_averages)(\n",
+    "            df, \n",
+    "            group_cols,\n",
+    "            metric_type = \"segment_speeds\"\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c007925-36e4-4842-b75f-2d81d596f313",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_spds = avg_speeds.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6debb886-1690-4e46-a674-6aa6ec81f993",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_spds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "628a1a82-1164-48f2-ba76-5a9126e8bc75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_spds = avg_spds.pipe(\n",
+    "            gtfs_schedule_wrangling.merge_operator_identifiers, \n",
+    "            analysis_date_list,\n",
+    "            columns = aseg.CROSSWALK_COLS\n",
+    "        )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/rt_segment_speeds/logs/avg_speeds.log b/rt_segment_speeds/logs/avg_speeds.log
index 26dd87007..b6880f7a2 100644
--- a/rt_segment_speeds/logs/avg_speeds.log
+++ b/rt_segment_speeds/logs/avg_speeds.log
@@ -678,3 +678,9 @@
 2025-01-16 16:28:31.739 | INFO     | __main__:summary_average_speeds:154 - rt_stop_times summary speed averaging for ['2023-04-15'] execution time: 0:00:17.603240
 2025-01-16 16:28:40.309 | INFO     | __main__:summary_average_speeds:120 - trip avg 0:00:08.508140
 2025-01-16 16:28:49.151 | INFO     | __main__:summary_average_speeds:154 - rt_stop_times summary speed averaging for ['2023-04-16'] execution time: 0:00:17.350031
+2025-01-21 17:41:50.589 | INFO     | __main__:segment_averages:187 - stop_segments segment averaging for ['2025-01-15'] execution time: 0:01:25.846519
+2025-01-21 18:38:00.086 | INFO     | __main__:summary_average_speeds:120 - trip avg 0:00:16.108454
+2025-01-21 18:38:13.628 | INFO     | __main__:summary_average_speeds:155 - rt_stop_times summary speed averaging for ['2025-01-15'] execution time: 0:00:29.651019
+2025-01-21 18:57:59.637 | INFO     | average_segment_speeds:segment_averages_detail:251 - speedmap_segments detailed segment averaging for ['2025-01-15'] execution time: 0:06:11.080021
+2025-01-21 19:03:28.422 | INFO     | average_segment_speeds:segment_averages:187 - speedmap_segments segment averaging for ['2025-01-15'] execution time: 0:05:28.685950
+2025-01-21 19:08:11.751 | INFO     | average_segment_speeds:segment_averages:187 - speedmap_segments segment averaging for ['2025-01-15'] execution time: 0:04:43.2400262025-02-12 10:11:30.775 | INFO     | __main__:segment_averages:187 - stop_segments segment averaging for ['2025-01-15'] execution time: 0:01:27.273686
diff --git a/rt_segment_speeds/logs/cut_stop_segments.log b/rt_segment_speeds/logs/cut_stop_segments.log
index 325748b96..902c77600 100644
--- a/rt_segment_speeds/logs/cut_stop_segments.log
+++ b/rt_segment_speeds/logs/cut_stop_segments.log
@@ -73,3 +73,7 @@
 2024-12-17 17:03:15.346 | INFO     | __main__:<module>:244 - speedmap segments and proxy_stop_times 2024-12-11: 0:05:24.205473
 2024-12-18 09:06:06.283 | INFO     | __main__:<module>:168 - cut segments 2024-12-11: 0:25:27.680094
 2024-12-18 09:13:13.266 | INFO     | __main__:<module>:244 - speedmap segments and proxy_stop_times 2024-12-11: 0:04:55.428371
+2025-01-21 13:14:13.570 | INFO     | __main__:<module>:168 - cut segments 2025-01-15: 0:28:00.641195
+2025-01-21 13:22:00.233 | INFO     | __main__:<module>:244 - speedmap segments and proxy_stop_times 2025-01-15: 0:05:55.448966
+2025-02-12 09:54:53.038 | INFO     | __main__:<module>:168 - cut segments 2025-01-15: 0:27:45.038713
+2025-02-12 10:02:11.788 | INFO     | __main__:<module>:244 - speedmap segments and proxy_stop_times 2025-01-15: 0:05:29.432920
diff --git a/rt_segment_speeds/logs/interpolate_stop_arrival.log b/rt_segment_speeds/logs/interpolate_stop_arrival.log
index 41ff1addc..f0ca5c395 100644
--- a/rt_segment_speeds/logs/interpolate_stop_arrival.log
+++ b/rt_segment_speeds/logs/interpolate_stop_arrival.log
@@ -137,3 +137,7 @@
 2024-12-18 10:52:54.623 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for stop_segments 2024-12-11:  2024-12-11: 0:10:20.061296
 2024-12-18 14:42:51.535 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for rt_stop_times 2024-12-11:  2024-12-11: 0:10:33.922215
 2024-12-18 14:56:54.836 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for speedmap_segments 2024-12-11:  2024-12-11: 0:02:36.918147
+2025-01-21 17:12:27.738 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for stop_segments 2025-01-15:  2025-01-15: 0:01:20.848747
+2025-01-21 18:35:30.275 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for rt_stop_times 2025-01-15:  2025-01-15: 0:13:06.847024
+2025-01-21 18:49:29.708 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for speedmap_segments 2025-01-15:  2025-01-15: 0:02:15.045884
+2025-02-12 10:08:28.509 | INFO     | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for stop_segments 2025-01-15:  2025-01-15: 0:01:23.252567
diff --git a/rt_segment_speeds/logs/nearest_vp.log b/rt_segment_speeds/logs/nearest_vp.log
index 8489eee65..cb89e3df9 100644
--- a/rt_segment_speeds/logs/nearest_vp.log
+++ b/rt_segment_speeds/logs/nearest_vp.log
@@ -276,3 +276,8 @@
 2024-12-18 10:42:34.461 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:189 - nearest neighbor for stop_segments 2024-12-11: 0:36:50.316084
 2024-12-18 14:32:17.520 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:189 - nearest neighbor for rt_stop_times 2024-12-11: 0:40:52.717197
 2024-12-18 14:54:17.879 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:189 - nearest neighbor for speedmap_segments 2024-12-11: 0:08:09.784862
+2025-01-21 13:27:30.117 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:188 - nearest neighbor for stop_segments 2025-01-15: 0:05:06.574214
+2025-01-21 17:11:06.855 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:188 - nearest neighbor for stop_segments 2025-01-15: 0:04:14.577150
+2025-01-21 18:22:23.308 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:188 - nearest neighbor for rt_stop_times 2025-01-15: 0:39:52.283432
+2025-01-21 18:47:14.614 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:188 - nearest neighbor for speedmap_segments 2025-01-15: 0:08:43.206096
+2025-02-12 10:07:05.221 | INFO     | nearest_vp_to_stop:nearest_neighbor_for_stop:188 - nearest neighbor for stop_segments 2025-01-15: 0:04:31.737901
diff --git a/rt_segment_speeds/logs/speeds_by_segment_trip.log b/rt_segment_speeds/logs/speeds_by_segment_trip.log
index fc97d8640..9263200e8 100644
--- a/rt_segment_speeds/logs/speeds_by_segment_trip.log
+++ b/rt_segment_speeds/logs/speeds_by_segment_trip.log
@@ -149,3 +149,7 @@
 2024-12-18 10:54:39.517 | INFO     | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for stop_segments 2024-12-11: 0:01:44.792095
 2024-12-18 14:44:34.009 | INFO     | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for rt_stop_times 2024-12-11: 0:01:42.393123
 2024-12-18 14:58:57.031 | INFO     | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for speedmap_segments 2024-12-11: 0:01:51.369378
+2025-01-21 17:13:56.681 | INFO     | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for stop_segments 2025-01-15: 0:01:28.844221
+2025-01-21 18:37:14.368 | INFO     | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for rt_stop_times 2025-01-15: 0:01:43.962507
+2025-01-21 18:51:30.158 | INFO     | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for speedmap_segments 2025-01-15: 0:01:50.399658
+2025-02-12 10:09:43.818 | INFO     | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for stop_segments 2025-01-15: 0:01:15.281024
diff --git a/rt_segment_speeds/scripts/average_segment_speeds.py b/rt_segment_speeds/scripts/average_segment_speeds.py
index c0f89426d..9ab3b4d14 100644
--- a/rt_segment_speeds/scripts/average_segment_speeds.py
+++ b/rt_segment_speeds/scripts/average_segment_speeds.py
@@ -72,20 +72,14 @@ def concatenate_trip_segment_speeds(
     ).pipe(
         gtfs_schedule_wrangling.add_peak_offpeak_column
     )
-    """
-    Amanda: There's already a `service_date` column.
-    df = df.rename(columns={'arrival_time':'service_date'}
+    # df = df.rename(columns={'arrival_time':'service_date'} #  will now cause errors by adding a second service_date col
+    #  drop arrival time if not needed, no need to rename to service_date since that now comes via concatenate_datasets_across_dates
+    df = df.drop(columns=['arrival_time']
     ).pipe(
         gtfs_schedule_wrangling.add_weekday_weekend_column
-    ) #  drop service_date?
+    )
     print("concatenated files") 
-    """ 
-    df = df.pipe(
-        gtfs_schedule_wrangling.add_weekday_weekend_column
-    ) #  drop service_date?
     
-    df.direction_id = df.direction_id.fillna(0)
-    print("concatenated files") 
     return df
 
 
@@ -104,9 +98,6 @@ def merge_in_segment_geometry(
         f"{SEGMENT_GCS}{SEGMENT_FILE}_{analysis_date}.parquet",
     ).to_crs(WGS84)
     
-    # Amanda: go back to the script that creates segment_geom to fill in nans
-    segment_geom.direction_id = segment_geom.direction_id.fillna(0)
-    
     col_order = [c for c in speeds_by_segment.columns]
     
     # The merge columns list should be all the columns that are in common
@@ -145,9 +136,6 @@ def segment_averages(
         get_pandas = False
     )
     
-    # Amanda, temporarily filling in direction id here 
-    df.direction_id = df.direction_id.fillna(0)
-    
     if weighted_averages:
         avg_speeds = delayed(metrics.concatenate_peak_offpeak_allday_averages)(
             df, 
diff --git a/rt_segment_speeds/scripts/nearest_vp_to_stop.py b/rt_segment_speeds/scripts/nearest_vp_to_stop.py
index 6e64d0a3f..f3ae2a8c0 100644
--- a/rt_segment_speeds/scripts/nearest_vp_to_stop.py
+++ b/rt_segment_speeds/scripts/nearest_vp_to_stop.py
@@ -171,7 +171,7 @@ def nearest_neighbor_for_stop(
         gdf.stop_meters
     )
 
-    gdf = gdf[trip_stop_cols + ["shape_array_key"]]
+    gdf = gdf[trip_stop_cols + ["shape_array_key", "stop_meters"]]
     
     gdf = gdf.assign(
         prior_vp_idx = vp_before,
diff --git a/rt_segment_speeds/segment_speed_utils/project_vars.py b/rt_segment_speeds/segment_speed_utils/project_vars.py
index cae8f5759..35b4118f5 100644
--- a/rt_segment_speeds/segment_speed_utils/project_vars.py
+++ b/rt_segment_speeds/segment_speed_utils/project_vars.py
@@ -11,14 +11,13 @@
 SHARED_GCS = GTFS_DATA_DICT.gcs_paths.SHARED_GCS
 PUBLIC_GCS = GTFS_DATA_DICT.gcs_paths.PUBLIC_GCS
 
-# analysis_date = rt_dates.DATES["nov2024"]
+analysis_date = rt_dates.DATES["jan2025"]
 
 oct2023_week = rt_dates.get_week("oct2023", exclude_wed=True)
 apr2023_week = rt_dates.get_week("apr2023", exclude_wed=True)
 apr2024_week = rt_dates.get_week("apr2024", exclude_wed=True)
 oct2024_week = rt_dates.get_week("oct2024", exclude_wed=True)
 
-# One file wasn't found for October 21 2024 
 all_dates = (
     rt_dates.y2024_dates + rt_dates.y2023_dates + 
     oct2024_week + apr2024_week + oct2023_week + apr2023_week
@@ -29,7 +28,8 @@
     rt_dates.oct2023_week, rt_dates.apr2023_week, 
 ]
 
-analysis_date_list =  apr2024_week + oct2023_week + apr2023_week
+
+analysis_date_list = [analysis_date]
                      
 
 PROJECT_CRS = "EPSG:3310"