Transit_Data_Calgary_2025.ipynb

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      "authorship_tag": "ABX9TyPPyv0AYFk13Gtm+WZ56/lb",
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      "name": "python"
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        "<a href=\"https://colab.research.google.com/github/Tanu-N-Prabhu/Python/blob/master/Transit_Data_Calgary_2025.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "cell_type": "markdown",
      "source": [
        "# Cracking Transit Data — Calgary 2025\n",
        "## How to Decode and Leverage GTFS for Real-Time Transit Insights\n",
        "\n",
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)\n",
        "\n",
        "\n",
        "\n",
        "      "
      ],
      "metadata": {
        "id": "9HGnYbLacYhV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction\n",
        "In the digital age, public transportation systems increasingly leverage technology to provide passengers with real-time data, enabling a more seamless travel experience. Transit systems share this data primarily through the General Transit Feed Specification (GTFS), a standard that provides data on schedules, routes, and real-time updates. However, while this data can be incredibly valuable, accessing and interpreting it can be challenging, especially when combining static and real-time feeds.\n",
        "\n",
        "This article shows you how to use Calgary Transit’s data about bus locations and routes to get useful information. If you work with data, understanding these datasets can help improve transit planning and make it better for users. We’ll cover the essential steps for fetching and processing Calgary Transit’s static and real-time data, including troubleshooting common issues you might encounter. By the end of this guide, you’ll be well-equipped to tap into Calgary’s transit data to solve real-world problems.\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "AgUWBNxpeVOB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GTFS\n",
        "The General Transit Feed Specification (GTFS) is an open standard that formats public transport schedules and geographic data. GTFS allows public transit agencies to publish their data in a format that various software applications can consume, such as trip planners and API developers. This allows users easy access to travel information on smartphones and other devices.\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "7ztDPf_wePoF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "GTFS includes information such as\n",
        "\n",
        "* Trip Name\n",
        "* Stops\n",
        "* Bus Routes\n",
        "* Fares\n",
        "* Time and more.\n",
        "\n",
        "When working with GTFS data, it’s important to understand the source and format of the data you are using. To fully explore and use the transit’s GTFS feeds, including the static and real-time data, visit their official website.\n",
        "\n",
        "[What is GTFS - General Transit Feed Specification](https://gtfs.org/getting-started/what-is-GTFS/?source=post_page-----49baea30833e--------------------------------)\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "4uEO0li5e_D5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Portal\n",
        "We can use Calgary’s Open Data Portal to get real-time transit vehicle locations. Calgary Transit provides real-time data through the General Transit Feed Specification Realtime (GTFS-RT) format. These feeds offer live information on vehicle positions, trip updates, and service alerts.\n",
        "\n",
        "## Available GTFS-RT Feeds\n",
        "* Vehicle Positions: Provides real-time locations of transit vehicles.\n",
        "* Trip Updates: Offers real-time updates on scheduled trips, including delays and cancellations.\n",
        "* Service Alerts: Contains information on disruptions or changes in service.\n",
        "\n",
        "## Accessing the Feeds\n",
        "These feeds are accessible via Calgary’s Open Data Portal:\n",
        "\n",
        "* Vehicle Positions Feed — [Calgary Open Data](https://data.calgary.ca/Transportation-Transit/Calgary-Transit-Realtime-Vehicle-Positions-GTFS-RT/am7c-qe3u?utm_source=chatgpt.com)\n",
        "* Trip Updates Feed — [Calgary Open Data](https://data.calgary.ca/Transportation-Transit/Calgary-Transit-Realtime-Trip-Updates-GTFS-RT/gs4m-mdc2?utm_source=chatgpt.com)\n",
        "* Service Alerts — [Calgary Open Data](https://data.calgary.ca/Transportation-Transit/Calgary-Transit-Realtime-Service-Alerts-GTFS-RT/jhgn-ynqj)\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "DogTaGHIfZny"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Vehicle Positions Feed\n",
        "## Handling GTFS-RT Feeds with Python\n",
        "Install Required Libraries"
      ],
      "metadata": {
        "id": "KQlP0WNOgFlk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install requests protobuf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2QVMut1gLbH",
        "outputId": "2b84bd07-adc0-49e9-e907-64769e02144d"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (2.32.3)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.11/dist-packages (4.25.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests) (2024.12.14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The requests library is used to send HTTP requests in Python. Whereas the protobuf is a library for working with Protocol Buffers (Protobuf), a method developed by Google for serializing structured data. Long story short, if you are handling GTFS-RT feed, then requests is used to fetch and protobuf is used to parse the data from an API.\n",
        "\n",
        "## Generating Python code from a Protocol Buffer\n",
        "We need to use the Protocol buffer compiler protoc1, a tool provided by Google for working with .proto files. We shall use the gtfs-realtime.proto Protobuf definition file. It describes the structure of GTFS-RT messages, including FeedMessage, FeedHeader, FeedEntity, etc. The technical documentation follows below.\n",
        "\n",
        "[Protobuf - General Transit Feed Specification](https://gtfs.org/documentation/realtime/proto/?source=post_page-----49baea30833e--------------------------------)"
      ],
      "metadata": {
        "id": "VNGi1KNQgQoI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!protoc --python_out=. gtfs-realtime.proto"
      ],
      "metadata": {
        "id": "VBlxVP1RgdUA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running this command creates a Python module, which isgtfs_realtime_pb2.py. If you don’t want the hassle, you can skip this step by just manually uploading the gtfs_realtime_pb2.py to your drive. Click [here](https://github.com/MobilityData/gtfs-realtime-bindings/blob/master/python/google/transit/gtfs_realtime_pb2.py) to access the file. To upload a file on Google Colab, you can write as:"
      ],
      "metadata": {
        "id": "K8aOcJWegfcG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FqZbVs1Tgncg",
        "outputId": "aec00674-33c9-4ad5-86aa-96b768be5993"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8ad9f7d9-4c2a-4934-a15c-39dd7ffc2452\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8ad9f7d9-4c2a-4934-a15c-39dd7ffc2452\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving gtfs_realtime_pb2.py to gtfs_realtime_pb2.py\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'gtfs_realtime_pb2.py': b'#! /usr/bin/python\\n#\\n# Copyright 2016-2019 Google Inc., MobilityData\\n#\\n# Licensed under the Apache License, Version 2.0 (the \"License\");\\n# you may not use this file except in compliance with the License.\\n#  You may obtain a copy of the License at\\n#\\n#     http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \"AS IS\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n\\n# -*- coding: utf-8 -*-\\n# Generated by the protocol buffer compiler.  DO NOT EDIT!\\n# source: gtfs-realtime.proto\\n\"\"\"Generated protocol buffer code.\"\"\"\\nfrom google.protobuf.internal import builder as _builder\\nfrom google.protobuf import descriptor as _descriptor\\nfrom google.protobuf import descriptor_pool as _descriptor_pool\\nfrom google.protobuf import symbol_database as _symbol_database\\n# @@protoc_insertion_point(imports)\\n\\n_sym_db = _symbol_database.Default()\\n\\n\\n\\n\\nDESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b\\'\\\\n\\\\x13gtfs-realtime.proto\\\\x12\\\\x10transit_realtime\\\\\"y\\\\n\\\\x0b\\\\x46\\\\x65\\\\x65\\\\x64Message\\\\x12,\\\\n\\\\x06header\\\\x18\\\\x01 \\\\x02(\\\\x0b\\\\x32\\\\x1c.transit_realtime.FeedHeader\\\\x12,\\\\n\\\\x06\\\\x65ntity\\\\x18\\\\x02 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_TRIPDESCRIPTOR_SCHEDULERELATIONSHIP._serialized_start=4403\\n  _TRIPDESCRIPTOR_SCHEDULERELATIONSHIP._serialized_end=4519\\n  _VEHICLEDESCRIPTOR._serialized_start=4537\\n  _VEHICLEDESCRIPTOR._serialized_end=4622\\n  _ENTITYSELECTOR._serialized_start=4625\\n  _ENTITYSELECTOR._serialized_end=4801\\n  _TRANSLATEDSTRING._serialized_start=4804\\n  _TRANSLATEDSTRING._serialized_end=4970\\n  _TRANSLATEDSTRING_TRANSLATION._serialized_start=4893\\n  _TRANSLATEDSTRING_TRANSLATION._serialized_end=4954\\n# @@protoc_insertion_point(module_scope)\\n'}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Your directory should look something like this. Use the !ls command.\n",
        "\n"
      ],
      "metadata": {
        "id": "vnD48OhWg4UO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3fJU2HggtWT",
        "outputId": "f25a1326-655f-4f91-96f0-788725735656"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drive  gtfs_realtime_pb2.py  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The whole point of using gtfs_realtime_pb2.py is to provide Python classes and methods that make it easy to work with GTFS-RT Protobuf (Protocol Buffers) encoded data. Without it, manual parsing and interpretation of the binary data would be necessary, which is both error-prone and impractical.\n",
        "\n",
        "## Fetch Calgary Transit’s GTFS-RT Feed\n",
        "Now let’s fetch and parse the data from the feed. Try uploading the gtfs_realtime_pb2.pymanually because if there is a mismatch between versions of the .proto file, then that will lead to a TypeError."
      ],
      "metadata": {
        "id": "DHCw3gIOg7s9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the requests library to handle HTTP requests\n",
        "import requests\n",
        "import pandas as pd\n",
        "import gtfs_realtime_pb2\n",
        "\n",
        "def fetch_gtfs_rt_feed(url):\n",
        "    \"\"\"\n",
        "    Fetches GTFS real-time data from the given URL.\n",
        "    Args:\n",
        "        url (str): The URL to fetch the GTFS real-time data from.\n",
        "    Returns:\n",
        "        feed: A GTFS-RT FeedMessage object containing the parsed data, or None if an error occurs.\n",
        "    \"\"\"\n",
        "    try:\n",
        "        # Send a GET request to the provided URL\n",
        "        response = requests.get(url)\n",
        "\n",
        "        # Check if the response status code is 200 (OK)\n",
        "        if response.status_code == 200:\n",
        "            # Initialize a FeedMessage object from gtfs_realtime_pb2\n",
        "            feed = gtfs_realtime_pb2.FeedMessage()\n",
        "\n",
        "            # Parse the response content into the FeedMessage object\n",
        "            feed.ParseFromString(response.content)\n",
        "            return feed\n",
        "        else:\n",
        "            # Print an error message if the status code is not 200\n",
        "            print(f\"Error fetching data: {response.status_code} - {response.reason}\")\n",
        "            return None\n",
        "    except Exception as e:\n",
        "        # Print an error message if an exception occurs\n",
        "        print(f\"An error occurred: {e}\")\n",
        "        return None\n",
        "# URL for GTFS real-time vehicle positions data\n",
        "vehicle_positions_url = \"https://data.calgary.ca/download/am7c-qe3u/application%2Foctet-stream\"\n",
        "# Fetch the GTFS real-time data from the specified URL\n",
        "feed = fetch_gtfs_rt_feed(vehicle_positions_url)\n",
        "# Check if the feed was fetched successfully\n",
        "if feed:\n",
        "    vehicle_data = []  # Initialize an empty list to store vehicle information\n",
        "\n",
        "    # Loop through the first 5 entities in the feed\n",
        "    for entity in feed.entity[:5]:  # [:5] ensures we only process the first 5 entities\n",
        "        if entity.HasField('vehicle'):  # Check if the entity contains vehicle data\n",
        "            vehicle = entity.vehicle  # Extract the vehicle field\n",
        "\n",
        "            # Create a dictionary with relevant vehicle information\n",
        "            vehicle_info = {\n",
        "                \"Vehicle ID\": vehicle.vehicle.id,  # Vehicle identifier\n",
        "                \"Latitude\": vehicle.position.latitude,  # Latitude position of the vehicle\n",
        "                \"Longitude\": vehicle.position.longitude  # Longitude position of the vehicle\n",
        "            }\n",
        "\n",
        "            # Append the vehicle information dictionary to the list\n",
        "            vehicle_data.append(vehicle_info)\n",
        "\n",
        "    # Create a DataFrame from the list of dictionaries\n",
        "    df = pd.DataFrame(vehicle_data)\n",
        "\n",
        "    # Print the DataFrame to display the data in a tabular format\n",
        "    print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "npB3t6C8g_qN",
        "outputId": "6c14e8b5-ba0c-4f94-94d8-b4bc76bc1619"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Vehicle ID   Latitude   Longitude\n",
            "0       8241  51.071911 -113.981918\n",
            "1       1200  51.057251 -114.020523\n",
            "2       8312  51.121761 -114.071648\n",
            "3       8274  51.123436 -114.071243\n",
            "4       8254  51.092236 -114.033279\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explanation\n",
        "First, you are using the requests library to fetch the data from the URL. Second, you execute the function fetch_gtfs_rt_feed that retrieves and parses the GTFS real-time feed. Third, you check the response to ensure successful data retrieval. Fourth, the system implements error handling to catch and print any errors. Finally, we then process the feed, which extracts and prints vehicle information like ID and position if available. To neaten the results, I used the Pandas library to display the data.\n",
        "\n",
        "## Output"
      ],
      "metadata": {
        "id": "BEbxNsuchSLQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "frJUgObkhQCy",
        "outputId": "2ce6e1f7-f008-445c-e098-e7ff74d3b1bf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Vehicle ID   Latitude   Longitude\n",
              "0       8241  51.071911 -113.981918\n",
              "1       1200  51.057251 -114.020523\n",
              "2       8312  51.121761 -114.071648\n",
              "3       8274  51.123436 -114.071243\n",
              "4       8254  51.092236 -114.033279"
            ],
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9287bbb3-4098-4afc-9da2-3e30347f5090 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f746a6ad-d9b6-4b38-9bea-a006ef89104f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f746a6ad-d9b6-4b38-9bea-a006ef89104f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Vehicle ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"1200\",\n          \"8254\",\n          \"8312\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02948042780870442,\n        \"min\": 51.0572509765625,\n        \"max\": 51.123435974121094,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          51.0572509765625,\n          51.09223556518555,\n          51.121761322021484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03769688871650909,\n        \"min\": -114.07164764404297,\n        \"max\": -113.98191833496094,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -114.02052307128906,\n          -114.03327941894531,\n          -114.07164764404297\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data that you are seeing is raw real-time information that comprises vehicle ID and its position (Latitude and Longitude). In some cases, the vehicle ID might directly relate to the Bus number (For example Vehicle ID 1280 will be Bus 128). It all depends on the City’s style of encoding the data. Contact the City Commission to get more accurate results.\n",
        "\n",
        "## Mapping the Location\n",
        "Let’s use folium library to map the data. Folium easily visualizes data manipulated in Python. Visit the documentation below.\n",
        "\n",
        "[Folium Documentation](https://python-visualization.github.io/folium/latest/?source=post_page-----49baea30833e--------------------------------)\n",
        "\n",
        "You need to install the folium library\n",
        "\n"
      ],
      "metadata": {
        "id": "nuUHduIbhbyV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install folium"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXyJfkKGhmkt",
        "outputId": "8433f6b7-7521-417e-eb79-b87994af888d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: folium in /usr/local/lib/python3.11/dist-packages (0.19.4)\n",
            "Requirement already satisfied: branca>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from folium) (0.8.1)\n",
            "Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.11/dist-packages (from folium) (3.1.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from folium) (1.26.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from folium) (2.32.3)\n",
            "Requirement already satisfied: xyzservices in /usr/local/lib/python3.11/dist-packages (from folium) (2024.9.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2>=2.9->folium) (3.0.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->folium) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->folium) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->folium) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->folium) (2024.12.14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Folium is used to create interactive maps. The reason I am using it is that it’s simple and easy to understand. Let me know if you come across any other similar libraries that get the job done.\n",
        "\n"
      ],
      "metadata": {
        "id": "5ppAUlRXhrCh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import folium  # Importing the folium library to work with interactive maps\n",
        "\n",
        "def plot_vehicle_on_map(latitude, longitude, vehicle_id):\n",
        "    \"\"\"\n",
        "    Plots the vehicle's location on a map using its latitude, longitude, and ID.\n",
        "\n",
        "    Parameters:\n",
        "    latitude (float): The latitude of the vehicle's location.\n",
        "    longitude (float): The longitude of the vehicle's location.\n",
        "    vehicle_id (str): The unique identifier for the vehicle.\n",
        "\n",
        "    Returns:\n",
        "    folium.Map: A Folium map centered on the vehicle's location with a marker.\n",
        "    \"\"\"\n",
        "    # Create a map centered at the vehicle's location with a zoom level of 14\n",
        "    vehicle_map = folium.Map(location=[latitude, longitude], zoom_start=14)\n",
        "\n",
        "    # Add a marker to the map at the vehicle's location\n",
        "    folium.Marker(\n",
        "        location=[latitude, longitude],  # The latitude and longitude of the marker\n",
        "        popup=f\"Vehicle ID: {vehicle_id}\",  # Popup text to display when the marker is clicked\n",
        "        icon=folium.Icon(color=\"blue\", icon=\"bus\", prefix=\"fa\"),  # Custom icon for the marker\n",
        "    ).add_to(vehicle_map)  # Add the marker to the map\n",
        "\n",
        "    return vehicle_map  # Return the map object\n",
        "# Example data for a vehicle's location and ID\n",
        "latitude = 51.071911   # Example latitude value\n",
        "longitude = -113.981918   # Example longitude value\n",
        "vehicle_id = \"8241\"  # Example vehicle ID\n",
        "# Generate the map with the example vehicle data\n",
        "map_output = plot_vehicle_on_map(latitude, longitude, vehicle_id)"
      ],
      "metadata": {
        "id": "E9X90mbJhrs-"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Output\n",
        "Upon execution of the above code, you will get the exact location of the Bus with the ID of 8080. Now keep in mind this data is dynamic. According to the Calgary Data Portal, this data changes every 30 minutes or even sooner."
      ],
      "metadata": {
        "id": "U2AC7q5_h_kK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "map_output  # Display the map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "o6s8dShxiGXR",
        "outputId": "9821c1c2-ca0f-4d90-f589-0f991637733b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7a8becf3bfd0>"
            ],
            "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 srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;html&gt;\n",
              "&lt;head&gt;\n",
              "    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    \n",
              "        &lt;script&gt;\n",
              "            L_NO_TOUCH = false;\n",
              "            L_DISABLE_3D = false;\n",
              "        &lt;/script&gt;\n",
              "    \n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-3.7.1.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap-glyphicons.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
              "    \n",
              "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "            &lt;style&gt;\n",
              "                #map_767fbbee03b5bd01a3a82ea903256c95 {\n",
              "                    position: relative;\n",
              "                    width: 100.0%;\n",
              "                    height: 100.0%;\n",
              "                    left: 0.0%;\n",
              "                    top: 0.0%;\n",
              "                }\n",
              "                .leaflet-container { font-size: 1rem; }\n",
              "            &lt;/style&gt;\n",
              "        \n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;\n",
              "    \n",
              "    \n",
              "            &lt;div class=&quot;folium-map&quot; id=&quot;map_767fbbee03b5bd01a3a82ea903256c95&quot; &gt;&lt;/div&gt;\n",
              "        \n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;\n",
              "    \n",
              "    \n",
              "            var map_767fbbee03b5bd01a3a82ea903256c95 = L.map(\n",
              "                &quot;map_767fbbee03b5bd01a3a82ea903256c95&quot;,\n",
              "                {\n",
              "                    center: [51.071911, -113.981918],\n",
              "                    crs: L.CRS.EPSG3857,\n",
              "                    ...{\n",
              "  &quot;zoom&quot;: 14,\n",
              "  &quot;zoomControl&quot;: true,\n",
              "  &quot;preferCanvas&quot;: false,\n",
              "}\n",
              "\n",
              "                }\n",
              "            );\n",
              "\n",
              "            \n",
              "\n",
              "        \n",
              "    \n",
              "            var tile_layer_9219a90c3378a5b9df665993fdfe62d4 = L.tileLayer(\n",
              "                &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
              "                {\n",
              "  &quot;minZoom&quot;: 0,\n",
              "  &quot;maxZoom&quot;: 19,\n",
              "  &quot;maxNativeZoom&quot;: 19,\n",
              "  &quot;noWrap&quot;: false,\n",
              "  &quot;attribution&quot;: &quot;\\u0026copy; \\u003ca href=\\&quot;https://www.openstreetmap.org/copyright\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e contributors&quot;,\n",
              "  &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "  &quot;detectRetina&quot;: false,\n",
              "  &quot;tms&quot;: false,\n",
              "  &quot;opacity&quot;: 1,\n",
              "}\n",
              "\n",
              "            );\n",
              "        \n",
              "    \n",
              "            tile_layer_9219a90c3378a5b9df665993fdfe62d4.addTo(map_767fbbee03b5bd01a3a82ea903256c95);\n",
              "        \n",
              "    \n",
              "            var marker_3a225e37589e410a3474c90a82e41bff = L.marker(\n",
              "                [51.071911, -113.981918],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_767fbbee03b5bd01a3a82ea903256c95);\n",
              "        \n",
              "    \n",
              "            var icon_23ffa7e61616f660dbf1048fab4b824a = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;bus&quot;,\n",
              "  &quot;prefix&quot;: &quot;fa&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_3a225e37589e410a3474c90a82e41bff.setIcon(icon_23ffa7e61616f660dbf1048fab4b824a);\n",
              "        \n",
              "    \n",
              "        var popup_03258795ccb0519fdfe50a22787dd088 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_46383d975f4205e0e9ff92e78af40e39 = $(`&lt;div id=&quot;html_46383d975f4205e0e9ff92e78af40e39&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Vehicle ID: 8241&lt;/div&gt;`)[0];\n",
              "                popup_03258795ccb0519fdfe50a22787dd088.setContent(html_46383d975f4205e0e9ff92e78af40e39);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_3a225e37589e410a3474c90a82e41bff.bindPopup(popup_03258795ccb0519fdfe50a22787dd088)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "&lt;/script&gt;\n",
              "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Trip Updates Feed\n",
        "The GTFS Realtime Trip Updates feed contains information about real-time updates to scheduled trips, such as delays, changes in stop times, and other dynamic data. But in this case, the Calgary real-time feed only provides Trip ID, Start Time, Start Date, Stop ID, Arrival Time, and Departure Time. The feed design reflects trip delays; for example, a trip scheduled for 8:00 AM with a 10-minute delay will show this updated information. The update will also include this ID if bus 8080 is running the trip."
      ],
      "metadata": {
        "id": "ae-UQFeGiKP0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import gtfs_realtime_pb2  # Import the compiled GTFS Realtime protocol buffer\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "\n",
        "def fetch_gtfs_rt_trip_updates(url):\n",
        "    \"\"\"Fetches and parses the GTFS Realtime Trip Updates feed from the given URL.\"\"\"\n",
        "    try:\n",
        "        # Make a GET request to fetch the data from the specified URL\n",
        "        response = requests.get(url)\n",
        "        if response.status_code == 200:\n",
        "            # Parse the response content into a FeedMessage object\n",
        "            feed = gtfs_realtime_pb2.FeedMessage()\n",
        "            feed.ParseFromString(response.content)\n",
        "            return feed\n",
        "        else:\n",
        "            # Print an error message if the response status is not OK\n",
        "            print(f\"Error fetching data: {response.status_code} - {response.reason}\")\n",
        "            return None\n",
        "    except Exception as e:\n",
        "        # Catch and print any exceptions that occur during the request\n",
        "        print(f\"An error occurred: {e}\")\n",
        "        return None\n",
        "def extract_trip_updates(feed):\n",
        "    \"\"\"Extracts trip update information from the GTFS Realtime feed.\"\"\"\n",
        "    trip_updates = []\n",
        "\n",
        "    # Loop through each entity in the feed\n",
        "    for entity in feed.entity:\n",
        "        if entity.HasField('trip_update'):\n",
        "            # Extract the trip update data\n",
        "            trip_update = entity.trip_update\n",
        "            trip_id = trip_update.trip.trip_id\n",
        "            start_time = trip_update.trip.start_time\n",
        "            start_date = trip_update.trip.start_date\n",
        "\n",
        "            # Loop through each stop time update in the trip update\n",
        "            for stop_time_update in trip_update.stop_time_update:\n",
        "                stop_id = stop_time_update.stop_id\n",
        "                # Extract arrival and departure times, if available\n",
        "                arrival_time = stop_time_update.arrival.time if stop_time_update.HasField('arrival') else None\n",
        "                departure_time = stop_time_update.departure.time if stop_time_update.HasField('departure') else None\n",
        "\n",
        "                # Convert timestamps to human-readable format\n",
        "                arrival_time = datetime.utcfromtimestamp(arrival_time).strftime('%Y-%m-%d %H:%M:%S') if arrival_time else None\n",
        "                departure_time = datetime.utcfromtimestamp(departure_time).strftime('%Y-%m-%d %H:%M:%S') if departure_time else None\n",
        "\n",
        "                # Add the extracted information to the trip updates list\n",
        "                trip_updates.append({\n",
        "                    \"Trip ID\": trip_id,\n",
        "                    \"Start Time\": start_time,\n",
        "                    \"Start Date\": start_date,\n",
        "                    \"Stop ID\": stop_id,\n",
        "                    \"Arrival Time\": arrival_time,\n",
        "                    \"Departure Time\": departure_time\n",
        "                })\n",
        "    return trip_updates\n",
        "# URL for GTFS Realtime Trip Updates\n",
        "trip_updates_url = \"https://data.calgary.ca/download/gs4m-mdc2/application%2Foctet-stream\"  # Replace with the actual Trip Updates URL\n",
        "# Fetch the trip updates feed\n",
        "feed = fetch_gtfs_rt_trip_updates(trip_updates_url)\n",
        "if feed:\n",
        "    # Extract the trip updates from the feed\n",
        "    trip_updates = extract_trip_updates(feed)\n",
        "\n",
        "    # Convert the trip updates into a DataFrame for easy manipulation and display\n",
        "    df_trip_updates = pd.DataFrame(trip_updates)\n",
        "\n",
        "    # Display the first 10 rows of the DataFrame\n",
        "    print(df_trip_updates.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "em0SgaSQiOLY",
        "outputId": "b8ade906-5939-4982-9997-3d931b5aabc5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Trip ID Start Time Start Date Stop ID         Arrival Time  \\\n",
            "0  69082338                          6212  2025-01-17 04:33:00   \n",
            "1  69082338                          4343  2025-01-17 04:34:00   \n",
            "2  69082338                          6674  2025-01-17 04:35:00   \n",
            "3  69082338                          6213  2025-01-17 04:36:00   \n",
            "4  69082338                          4342  2025-01-17 04:37:00   \n",
            "5  69082338                          6214  2025-01-17 04:38:00   \n",
            "6  69082338                          6962  2025-01-17 04:39:00   \n",
            "7  69082338                          6705  2025-01-17 04:41:00   \n",
            "8  69082338                          6963  2025-01-17 04:41:00   \n",
            "9  69082338                          6964  2025-01-17 04:42:00   \n",
            "\n",
            "        Departure Time  \n",
            "0  2025-01-17 04:33:00  \n",
            "1  2025-01-17 04:34:00  \n",
            "2  2025-01-17 04:35:00  \n",
            "3  2025-01-17 04:36:00  \n",
            "4  2025-01-17 04:37:00  \n",
            "5  2025-01-17 04:38:00  \n",
            "6  2025-01-17 04:39:00  \n",
            "7  2025-01-17 04:41:00  \n",
            "8  2025-01-17 04:41:00  \n",
            "9  2025-01-17 04:42:00  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Output"
      ],
      "metadata": {
        "id": "DC-W9Dguh6a9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_trip_updates.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "h-C5IRotiTAv",
        "outputId": "63cd07f1-19a3-40b0-945e-a7130dd927e5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Trip ID Start Time Start Date Stop ID         Arrival Time  \\\n",
              "0  69082338                          6212  2025-01-17 04:33:00   \n",
              "1  69082338                          4343  2025-01-17 04:34:00   \n",
              "2  69082338                          6674  2025-01-17 04:35:00   \n",
              "3  69082338                          6213  2025-01-17 04:36:00   \n",
              "4  69082338                          4342  2025-01-17 04:37:00   \n",
              "5  69082338                          6214  2025-01-17 04:38:00   \n",
              "6  69082338                          6962  2025-01-17 04:39:00   \n",
              "7  69082338                          6705  2025-01-17 04:41:00   \n",
              "8  69082338                          6963  2025-01-17 04:41:00   \n",
              "9  69082338                          6964  2025-01-17 04:42:00   \n",
              "\n",
              "        Departure Time  \n",
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              "2  2025-01-17 04:35:00  \n",
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              "6  2025-01-17 04:39:00  \n",
              "7  2025-01-17 04:41:00  \n",
              "8  2025-01-17 04:41:00  \n",
              "9  2025-01-17 04:42:00  "
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              "\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-75261600-8d86-428f-b17c-bcf8df36e287 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_trip_updates",
              "summary": "{\n  \"name\": \"df_trip_updates\",\n  \"rows\": 10999,\n  \"fields\": [\n    {\n      \"column\": \"Trip ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 554,\n        \"samples\": [\n          \"69083977\",\n          \"69083929\",\n          \"69088118\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Start Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Start Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stop ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5198,\n        \"samples\": [\n          \"4652\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Arrival Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 105,\n        \"samples\": [\n          \"2025-01-17 05:14:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Departure Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 102,\n        \"samples\": [\n          \"2025-01-17 05:14:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you’re not receiving start_time and start_date from the GTFS Realtime Trip Updates, it might be because those fields are optional and not always provided in the feed. This might be for security reasons as well. Upon contacting the City’s Transit service, you can get this vital information. I hope you understand the point.\n",
        "\n",
        "[Calgary Transit Realtime Trip Updates GTFS-RT](https://data.calgary.ca/Transportation-Transit/Calgary-Transit-Realtime-Trip-Updates-GTFS-RT/gs4m-mdc2/about_data?utm_source=chatgpt.com&source=post_page-----49baea30833e--------------------------------)\n",
        "\n",
        "---\n",
        "\n",
        "## Side Note\n",
        "\n",
        "You see how there are different IDs involved, such as Trip and the Stop. Now, if all the data is available, you can easily find the corresponding bus that has been running in those routes. Let me know if you can connect those dots. I would be happy to Colab with you and make this a working project.\n",
        "\n",
        "---\n",
        "\n",
        "# Service Alerts\n",
        "\n",
        "Calgary Transit refreshes its real-time data every half minute. To learn more about the [GTFS-RT specification](https://developers.google.com/transit/gtfs-realtime/) and its [components](https://developers.google.com/transit/gtfs-realtime/guides/feed-entities) ([Trip Updates](https://data.calgary.ca/dataset/GTFS-RT-Trip-Updates/gs4m-mdc2), [Service Alerts](https://data.calgary.ca/Transportation-Transit/Calgary-Transit-Realtime-Service-Alerts-GTFS-RT/jhgn-ynqj/about_data), and [Vehicle Positions](https://data.calgary.ca/dataset/GTFS-RT-Vehicle-Positions/am7c-qe3u)), check out the Google Transit API page. Also, see [Service Updates](http://www.calgarytransit.com/service-updates?nid=170214). Let’s see what the service alerts look like."
      ],
      "metadata": {
        "id": "efffCccxidRn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests  # Library to make HTTP requests\n",
        "import gtfs_realtime_pb2  # Ensure this proto file is compiled as Python\n",
        "import pandas as pd  # For handling and displaying data in DataFrame format\n",
        "\n",
        "# Function to fetch the GTFS Realtime Alerts feed\n",
        "def fetch_gtfs_rt_alerts(url):\n",
        "    \"\"\"Fetches and parses the GTFS Realtime Alerts feed from the given URL.\"\"\"\n",
        "    try:\n",
        "        # Send a request to the URL to get the feed\n",
        "        response = requests.get(url)\n",
        "\n",
        "        # Check if the response is successful (status code 200)\n",
        "        if response.status_code == 200:\n",
        "            # Parse the feed using GTFS Realtime protocol\n",
        "            feed = gtfs_realtime_pb2.FeedMessage()\n",
        "            feed.ParseFromString(response.content)\n",
        "            return feed  # Return the parsed feed\n",
        "        else:\n",
        "            # Print error if the response status is not 200\n",
        "            print(f\"Error fetching data: {response.status_code} - {response.reason}\")\n",
        "            return None\n",
        "    except Exception as e:\n",
        "        # Catch and print any exception that occurs during the request\n",
        "        print(f\"An error occurred: {e}\")\n",
        "        return None\n",
        "\n",
        "# Function to extract alerts from the GTFS Realtime feed\n",
        "def extract_alerts(feed):\n",
        "    \"\"\"Extracts alert information from the GTFS Realtime feed.\"\"\"\n",
        "    alerts = []  # Initialize an empty list to store alert information\n",
        "\n",
        "    # Loop through each entity in the feed\n",
        "    for entity in feed.entity:\n",
        "        # Check if the entity contains an alert\n",
        "        if entity.HasField('alert'):\n",
        "            alert = entity.alert\n",
        "            # Extract relevant fields from the alert\n",
        "            alert_id = entity.id  # Unique ID for the alert\n",
        "            # Extract header text from the alert (if available)\n",
        "            header_text = alert.header_text.translation[0].text if alert.header_text.translation else \"No header\"\n",
        "            # Extract description text from the alert (if available)\n",
        "            description_text = alert.description_text.translation[0].text if alert.description_text.translation else \"No description\"\n",
        "            severity_level = alert.severity_level  # Severity level of the alert (e.g., low, medium, high)\n",
        "\n",
        "            # Append the extracted alert information to the alerts list\n",
        "            alerts.append({\n",
        "                \"Alert ID\": alert_id,\n",
        "                \"Header\": header_text,\n",
        "                \"Description\": description_text,\n",
        "                \"Severity Level\": severity_level\n",
        "            })\n",
        "\n",
        "    # Return the list of alerts\n",
        "    return alerts\n",
        "\n",
        "# URL for GTFS Realtime Alerts (replace with the actual URL)\n",
        "alerts_url = \"https://data.calgary.ca/download/jhgn-ynqj/application%2Foctet-stream\"  # Example placeholder URL\n",
        "\n",
        "# Fetch the alerts feed\n",
        "feed = fetch_gtfs_rt_alerts(alerts_url)\n",
        "\n",
        "# If feed is fetched successfully, extract the alerts\n",
        "if feed:\n",
        "    alerts = extract_alerts(feed)\n",
        "\n",
        "    # Convert the list of alerts into a DataFrame for easier viewing\n",
        "    df_alerts = pd.DataFrame(alerts)\n",
        "\n",
        "    # Display the first 5 rows of the alerts DataFrame\n",
        "    print(df_alerts.head(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmeyPyCEi-lt",
        "outputId": "a80e6f35-8225-4e1c-9147-5eed6162b294"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Alert ID Header                                        Description  \\\n",
            "0   166173         <div data-effect=\"DETOUR\"></div>\\n<p>Starting ...   \n",
            "1   166174         <div data-effect=\"DETOUR\"></div><p>Starting Th...   \n",
            "2   166175         <div data-effect=\"STOP_MOVED\"></div>\\n<p>Start...   \n",
            "3   166177         <div data-effect=\"DETOUR\"></div>\\n<p>Starting ...   \n",
            "4   166178         <div data-effect=\"DETOUR\"></div>\\n<p>Starting ...   \n",
            "\n",
            "   Severity Level  \n",
            "0               1  \n",
            "1               1  \n",
            "2               1  \n",
            "3               1  \n",
            "4               1  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can read the documentation of the Service Alerts below.\n",
        "\n",
        "[Calgary Transit Realtime Service Alerts GTFS - RT](https://data.calgary.ca/Transportation-Transit/Calgary-Transit-Realtime-Service-Alerts-GTFS-RT/jhgn-ynqj/about_data?source=post_page-----49baea30833e--------------------------------)\n",
        "\n",
        "Upon executing the above code snippet, I noticed that the data was not in the readable format. The descriptions were not fully displayed and contained HTML tags. So that’s why I have used BeautifulSoup library to clean this up, you can trim leading and trailing spaces or newlines from the alert text.\n",
        "\n"
      ],
      "metadata": {
        "id": "u8RJ_PxxjOop"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup  # Import the BeautifulSoup library to parse and clean HTML\n",
        "\n",
        "def clean_html(raw_html):\n",
        "    \"\"\"Removes HTML tags and returns plain text.\"\"\"\n",
        "    # Create a BeautifulSoup object to parse the HTML content\n",
        "    soup = BeautifulSoup(raw_html, 'html.parser')\n",
        "    # Use the get_text() method to extract and return the plain text from the HTML\n",
        "    return soup.get_text()\n",
        "# Loop through each alert and clean the \"Header\" and \"Description\" fields by removing HTML tags\n",
        "for alert in alerts:\n",
        "    # Apply the clean_html function to the \"Header\" field\n",
        "    alert[\"Header\"] = clean_html(alert[\"Header\"])\n",
        "    # Apply the clean_html function to the \"Description\" field\n",
        "    alert[\"Description\"] = clean_html(alert[\"Description\"])\n",
        "# Convert the cleaned alerts into a pandas DataFrame for easy viewing and manipulation\n",
        "df_alerts = pd.DataFrame(alerts)\n",
        "# Display the first 5 rows of the DataFrame to verify the cleaned alerts\n",
        "print(df_alerts.head(5))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIEXmsrUjf6c",
        "outputId": "bad67a5e-265d-4c24-b644-1e2d2c455c23"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Alert ID Header                                        Description  \\\n",
            "0   166173         \\nStarting Thursday, January 16 at 9 a.m. thro...   \n",
            "1   166174         Starting Thursday, January 16 at 9 a.m. throug...   \n",
            "2   166175         \\nStarting Thursday, January 16 at 9 a.m. thro...   \n",
            "3   166177         \\nStarting Thursday, January 16 at 9 a.m. thro...   \n",
            "4   166178         \\nStarting Thursday, January 16 at 9 a.m. thro...   \n",
            "\n",
            "   Severity Level  \n",
            "0               1  \n",
            "1               1  \n",
            "2               1  \n",
            "3               1  \n",
            "4               1  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Output\n",
        "You might be asking yourself, “Can I associate this with the trip_id and vehicle_id”. The answer is Yes, by incorporating that information into your data processing, you can link the alerts to particular trip_id and vehicle_id. Every alert should be connected to the appropriate trip and vehicle. Once again, it’s a moving piece of the puzzle. Once the data is completely available for the public without encapsulation, this can be possible."
      ],
      "metadata": {
        "id": "i6yh1MbCjkL_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_alerts.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "W_5wtfRCjlxa",
        "outputId": "4ff0413f-534f-4151-8920-4526d5fdac16"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Alert ID Header                                        Description  \\\n",
              "0   166173         \\nStarting Thursday, January 16 at 9 a.m. thro...   \n",
              "1   166174         Starting Thursday, January 16 at 9 a.m. throug...   \n",
              "2   166175         \\nStarting Thursday, January 16 at 9 a.m. thro...   \n",
              "3   166177         \\nStarting Thursday, January 16 at 9 a.m. thro...   \n",
              "4   166178         \\nStarting Thursday, January 16 at 9 a.m. thro...   \n",
              "\n",
              "   Severity Level  \n",
              "0               1  \n",
              "1               1  \n",
              "2               1  \n",
              "3               1  \n",
              "4               1  "
            ],
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              "\n",
              "  <div id=\"df-93e4a052-03c8-4d5e-b1e4-29031f71d7b9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d85f03e9-2900-47db-8b05-d426d1d172a4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_alerts",
              "summary": "{\n  \"name\": \"df_alerts\",\n  \"rows\": 22,\n  \"fields\": [\n    {\n      \"column\": \"Alert ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"166173\",\n          \"165820\",\n          \"166075\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Header\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"\\nStarting Thursday, January 16 at 9 a.m. through Friday, January 17 at 3 p.m., 1 Street will be closed between 7 - 9 Avenue S.W. for construction.\\u00a0 Route 6 will be detoured.\\nRoute 6 will travel:\\n\\nNorth on 1 Street S.W.\\u00a0\\nEast on 9 Avenue S.W.\\u00a0\\nNorth on Centre Street S.\\nWest on 6 Avenue S.W. to regular route\\n\\n\\tThe following stop will be temporarily closed:\\n\\tSB 1 ST @ 7 AV SW (#5303)\\n\\n\\tPlease use the following stop:\\n    \\nNB Centre ST @ 7 AV S (farside) - detour stop\\n\\t \\n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Severity Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n",
        "You’ve made it to the end! This topic is vast and offers significant opportunities for further exploration and development. With the right approach, you could create a new application to help Calgary’s residents avoid inconveniences during bus travel, especially in extreme weather conditions. If you encounter any issues while executing the code, feel free to reach out. Suggestions are always welcome! I hope you enjoyed reading this article, and I look forward to seeing you next time. Happy coding!"
      ],
      "metadata": {
        "id": "Wt0wpn1Pjwop"
      }
    }
  ]
}