Skip to main content
`Ctrl`+`K`

Note

Go to the end to download the full example code.

# Import GTFS

In this example, we import a GTFS feed to our model and perform map matching.

We use data from Coquimbo, a city in La Serena Metropolitan Area in Chile.

See also

Several functions, methods, classes and modules are used in this example:

- `aequilibrae.transit.Transit()`

- `aequilibrae.transit.lib_gtfs.GTFSRouteSystemBuilder()`

    # Imports
    from uuid import uuid4
    from os import remove
    from os.path import join
    from tempfile import gettempdir

    import folium
    import pandas as pd
    from aequilibrae.project.database_connection import database_connection

    from aequilibrae.transit import Transit
    from aequilibrae.utils.create_example import create_example

    # Let's create an empty project on an arbitrary folder.
    fldr = join(gettempdir(), uuid4().hex)
    project = create_example(fldr, "coquimbo")

As the Coquimbo example already has a complete GTFS model, we shall remove its public transport database for the sake of this example.

    remove(join(fldr, "public_transport.sqlite"))

Let’s import the GTFS feed.

    dest_path = join(fldr, "gtfs_coquimbo.zip")

Now we create our Transit object and import the GTFS feed into our model. This will automatically create a new public transport database.

    data = Transit(project)

    transit = data.new_gtfs_builder(agency="Lisanco", file_path=dest_path)

To load the data, we must choose one date. We’re going to continue with 2016-04-13 but feel free to experiment with any other available dates. Transit class has a function allowing you to check dates for the GTFS feed. It should take approximately 2 minutes to load the data.

    transit.load_date("2016-04-13")

    # Now we execute the map matching to find the real paths.
    # Depending on the GTFS size, this process can be really time-consuming.
    transit.set_allow_map_match(True)
    transit.map_match()

    # Finally, we save our GTFS into our model.
    transit.save_to_disk()

    Loading routes (Step: 1/12)                       : 0it [00:00, ?it/s]

    Loading stops (Step: 2/12)                        :   0%|          | 0/78 [00:00<?, ?it/s]

    Loading stop times (Step: 3/12)                   :   0%|          | 0/1007 [00:00<?, ?it/s]
    Loading stop times (Step: 3/12)                   :  63%|██████▎   | 637/1007 [00:00<00:00, 4435.68it/s]

    Loading shapes (Step: 4/12)                       :   0%|          | 0/2 [00:00<?, ?it/s]

    Loading trips (Step: 5/12)                        :   0%|          | 0/1007 [00:00<?, ?it/s]
    Loading trips (Step: 5/12)                        :   4%|▍         | 42/1007 [00:00<00:02, 411.34it/s]
    Loading trips (Step: 5/12)                        :   8%|▊         | 84/1007 [00:00<00:02, 406.17it/s]
    Loading trips (Step: 5/12)                        :  13%|█▎        | 126/1007 [00:00<00:02, 412.27it/s]
    Loading trips (Step: 5/12)                        :  17%|█▋        | 168/1007 [00:00<00:02, 411.01it/s]
    Loading trips (Step: 5/12)                        :  21%|██        | 210/1007 [00:00<00:01, 409.72it/s]
    Loading trips (Step: 5/12)                        :  25%|██▌       | 252/1007 [00:00<00:01, 411.10it/s]
    Loading trips (Step: 5/12)                        :  29%|██▉       | 294/1007 [00:00<00:01, 410.02it/s]
    Loading trips (Step: 5/12)                        :  33%|███▎      | 336/1007 [00:00<00:01, 412.09it/s]
    Loading trips (Step: 5/12)                        :  38%|███▊      | 378/1007 [00:00<00:01, 410.93it/s]
    Loading trips (Step: 5/12)                        :  42%|████▏     | 420/1007 [00:01<00:01, 408.98it/s]
    Loading trips (Step: 5/12)                        :  46%|████▌     | 462/1007 [00:01<00:01, 410.24it/s]
    Loading trips (Step: 5/12)                        :  50%|█████     | 504/1007 [00:01<00:01, 405.31it/s]
    Loading trips (Step: 5/12)                        :  54%|█████▍    | 546/1007 [00:01<00:01, 409.42it/s]
    Loading trips (Step: 5/12)                        :  58%|█████▊    | 588/1007 [00:01<00:01, 412.42it/s]
    Loading trips (Step: 5/12)                        :  63%|██████▎   | 631/1007 [00:01<00:00, 416.41it/s]
    Loading trips (Step: 5/12)                        :  67%|██████▋   | 674/1007 [00:01<00:00, 420.29it/s]
    Loading trips (Step: 5/12)                        :  71%|███████   | 717/1007 [00:01<00:00, 300.82it/s]
    Loading trips (Step: 5/12)                        :  75%|███████▌  | 760/1007 [00:01<00:00, 329.34it/s]
    Loading trips (Step: 5/12)                        :  80%|███████▉  | 803/1007 [00:02<00:00, 352.50it/s]
    Loading trips (Step: 5/12)                        :  84%|████████▍ | 846/1007 [00:02<00:00, 369.06it/s]
    Loading trips (Step: 5/12)                        :  88%|████████▊ | 888/1007 [00:02<00:00, 381.17it/s]
    Loading trips (Step: 5/12)                        :  92%|█████████▏| 931/1007 [00:02<00:00, 392.66it/s]
    Loading trips (Step: 5/12)                        :  97%|█████████▋| 975/1007 [00:02<00:00, 403.78it/s]

    De-conflicting stop times (Step: 6/12)            :   0%|          | 0/1 [00:00<?, ?it/s]
    Loading fare data (Step: 7/12)                    : 100%|██████████| 1/1 [00:00<00:00,  1.64it/s]

    Loading data for 2016-04-13 (Step: 9/12) -        :   0%|          | 0/1 [00:00<?, ?it/s]

    Map-matching patterns                             :   0%|          | 0/2 [00:00<?, ?it/s]
    Creating map-matching graph from scratch for mode_id=3 (Step: 8/12):  50%|█████     | 1/2 [00:00<00:00,  6.43it/s]

    Building graphs - Indexing links (Step: 8/12)     : 0it [00:00, ?it/s]
    Building graphs - Indexing links (Step: 8/12)     : 873it [00:00, 8729.34it/s]
    Building graphs - Indexing links (Step: 8/12)     : 1746it [00:00, 8679.46it/s]
    Building graphs - Indexing links (Step: 8/12)     : 2614it [00:00, 8578.46it/s]
    Building graphs - Indexing links (Step: 8/12)     : 3472it [00:00, 8320.65it/s]
    Building graphs - Indexing links (Step: 8/12)     : 4306it [00:00, 8072.87it/s]
    Building graphs - Indexing links (Step: 8/12)     : 5115it [00:00, 7817.42it/s]
    Building graphs - Indexing links (Step: 8/12)     : 5899it [00:00, 7515.35it/s]
    Building graphs - Indexing links (Step: 8/12)     : 6653it [00:00, 7263.17it/s]
    Building graphs - Indexing links (Step: 8/12)     : 7382it [00:00, 7172.57it/s]
    Building graphs - Indexing links (Step: 8/12)     : 8133it [00:01, 7270.01it/s]
    Building graphs - Indexing links (Step: 8/12)     : 8898it [00:01, 7380.05it/s]
    Building graphs - Indexing links (Step: 8/12)     : 9651it [00:01, 7421.97it/s]
    Building graphs - Indexing links (Step: 8/12)     : 10395it [00:01, 7373.58it/s]
    Building graphs - Indexing links (Step: 8/12)     : 11134it [00:01, 7226.17it/s]
    Building graphs - Indexing links (Step: 8/12)     : 11858it [00:01, 7195.96it/s]
    Building graphs - Indexing links (Step: 8/12)     : 12579it [00:01, 7051.46it/s]
    Building graphs - Indexing links (Step: 8/12)     : 13285it [00:01, 6964.84it/s]
    Building graphs - Indexing links (Step: 8/12)     : 14022it [00:01, 7077.69it/s]
    Building graphs - Indexing links (Step: 8/12)     : 14761it [00:01, 7168.54it/s]
    Building graphs - Indexing links (Step: 8/12)     : 15479it [00:02, 7151.05it/s]
    Building graphs - Indexing links (Step: 8/12)     : 16195it [00:02, 7130.12it/s]
    Building graphs - Indexing links (Step: 8/12)     : 16909it [00:02, 7054.72it/s]
    Building graphs - Indexing links (Step: 8/12)     : 17615it [00:02, 6972.12it/s]
    Building graphs - Indexing links (Step: 8/12)     : 18336it [00:02, 7039.05it/s]
    Building graphs - Indexing links (Step: 8/12)     : 19041it [00:02, 6939.20it/s]
    Building graphs - Indexing links (Step: 8/12)     : 19765it [00:02, 7017.26it/s]
    Building graphs - Indexing links (Step: 8/12)     : 20468it [00:02, 6989.88it/s]
    Building graphs - Indexing links (Step: 8/12)     : 21170it [00:02, 6997.99it/s]
    Building graphs - Indexing links (Step: 8/12)     : 21929it [00:03, 7171.75it/s]
    Building graphs - Indexing links (Step: 8/12)     : 22647it [00:03, 7100.94it/s]
    Building graphs - Indexing links (Step: 8/12)     : 23358it [00:03, 7059.59it/s]
    Building graphs - Indexing links (Step: 8/12)     : 24084it [00:03, 7107.04it/s]
    Building graphs - Indexing links (Step: 8/12)     : 24795it [00:03, 7089.86it/s]
    Building graphs - Indexing links (Step: 8/12)     : 25558it [00:03, 7247.47it/s]
    Building graphs - Indexing links (Step: 8/12)     : 26283it [00:03, 7232.49it/s]
    Building graphs - Indexing links (Step: 8/12)     : 27030it [00:03, 7285.79it/s]
    Building graphs - Indexing links (Step: 8/12)     : 27759it [00:03, 7106.56it/s]
    Building graphs - Indexing links (Step: 8/12)     : 28471it [00:03, 7108.73it/s]
    Building graphs - Indexing links (Step: 8/12)     : 29208it [00:04, 7185.52it/s]
    Building graphs - Indexing links (Step: 8/12)     : 29928it [00:04, 7178.74it/s]
    Building graphs - Indexing links (Step: 8/12)     : 30647it [00:04, 7080.47it/s]
    Building graphs - Indexing links (Step: 8/12)     : 31356it [00:04, 7025.26it/s]
    Building graphs - Indexing links (Step: 8/12)     : 32070it [00:04, 7057.91it/s]
    Building graphs - Indexing links (Step: 8/12)     : 32783it [00:04, 7078.08it/s]
    Building graphs - Indexing links (Step: 8/12)     : 33523it [00:04, 7171.99it/s]
    Building graphs - Indexing links (Step: 8/12)     : 34241it [00:04, 7056.25it/s]

    Building graphs - Breaking links (Step: 8/12)     :   0%|          | 0/78 [00:00<?, ?it/s]
    Building graphs - Breaking links (Step: 8/12)     :  17%|█▋        | 13/78 [00:00<00:00, 119.32it/s]
    Building graphs - Breaking links (Step: 8/12)     :  35%|███▍      | 27/78 [00:00<00:00, 128.46it/s]
    Building graphs - Breaking links (Step: 8/12)     :  51%|█████▏    | 40/78 [00:00<00:00, 122.14it/s]
    Building graphs - Breaking links (Step: 8/12)     :  68%|██████▊   | 53/78 [00:00<00:00, 123.19it/s]
    Building graphs - Breaking links (Step: 8/12)     :  85%|████████▍ | 66/78 [00:00<00:00, 104.19it/s]
    Building graphs - Breaking links (Step: 8/12)     :  99%|█████████▊| 77/78 [00:00<00:00, 96.65it/s]
    Map-matching patterns                             :   3%|▎         | 2/78 [00:11<00:00, 96.65it/s]

    Saving patterns (Step: 10/12)                     :   0%|          | 0/2 [00:00<?, ?it/s]

    Saving trips (Step: 11/12)                        :   0%|          | 0/360 [00:00<?, ?it/s]

    Saving links (Step: 11/12)                        :   0%|          | 0/78 [00:00<?, ?it/s]

    Saving stops (Step: 12/12)                        :   0%|          | 0/78 [00:00<?, ?it/s]
    Saving stops (Step: 12/12)                        :   6%|▋         | 5/78 [00:00<00:01, 45.16it/s]
    Saving stops (Step: 12/12)                        :  13%|█▎        | 10/78 [00:00<00:01, 40.64it/s]
    Saving stops (Step: 12/12)                        :  19%|█▉        | 15/78 [00:00<00:01, 37.67it/s]
    Saving stops (Step: 12/12)                        :  24%|██▍       | 19/78 [00:00<00:01, 38.42it/s]
    Saving stops (Step: 12/12)                        :  31%|███       | 24/78 [00:00<00:01, 39.11it/s]
    Saving stops (Step: 12/12)                        :  37%|███▋      | 29/78 [00:00<00:01, 39.75it/s]
    Saving stops (Step: 12/12)                        :  44%|████▎     | 34/78 [00:00<00:01, 39.93it/s]
    Saving stops (Step: 12/12)                        :  50%|█████     | 39/78 [00:00<00:00, 39.92it/s]
    Saving stops (Step: 12/12)                        :  56%|█████▋    | 44/78 [00:01<00:00, 40.23it/s]
    Saving stops (Step: 12/12)                        :  63%|██████▎   | 49/78 [00:01<00:00, 40.14it/s]
    Saving stops (Step: 12/12)                        :  69%|██████▉   | 54/78 [00:01<00:00, 40.10it/s]
    Saving stops (Step: 12/12)                        :  76%|███████▌  | 59/78 [00:01<00:00, 40.35it/s]
    Saving stops (Step: 12/12)                        :  82%|████████▏ | 64/78 [00:01<00:00, 40.17it/s]
    Saving stops (Step: 12/12)                        :  88%|████████▊ | 69/78 [00:01<00:00, 40.14it/s]
    Saving stops (Step: 12/12)                        :  95%|█████████▍| 74/78 [00:01<00:00, 40.35it/s]

Now we will plot one of the route’s patterns we just imported

    conn = database_connection("transit")

    links = pd.read_sql("SELECT pattern_id, ST_AsText(geometry) geom FROM routes;", con=conn)

    stops = pd.read_sql("""SELECT stop_id, ST_X(geometry) X, ST_Y(geometry) Y FROM stops""", con=conn)

    gtfs_links = folium.FeatureGroup("links")
    gtfs_stops = folium.FeatureGroup("stops")

    layers = [gtfs_links, gtfs_stops]

    pattern_colors = ["#146DB3", "#EB9719"]

    for i, row in links.iterrows():
        points = row.geom.replace("MULTILINESTRING", "").replace("(", "").replace(")", "").split(", ")
        points = "[[" + "],[".join([p.replace(" ", ", ") for p in points]) + "]]"
        points = [[x[1], x[0]] for x in eval(points)]

        _ = folium.vector_layers.PolyLine(
            points,
            popup=f"<b>pattern_id: {row.pattern_id}</b>",
            color=pattern_colors[i],
            weight=5,
        ).add_to(gtfs_links)

    for i, row in stops.iterrows():
        point = (row.Y, row.X)

        _ = folium.vector_layers.CircleMarker(
            point,
            popup=f"<b>stop_id: {row.stop_id}</b>",
            color="black",
            radius=2,
            fill=True,
            fillColor="black",
            fillOpacity=1.0,
        ).add_to(gtfs_stops)

Let’s create the map!

    map_osm = folium.Map(location=[-29.93, -71.29], zoom_start=13)

    # add all layers
    for layer in layers:
        layer.add_to(map_osm)

    # And add layer control before we display it
    folium.LayerControl().add_to(map_osm)
    map_osm

Make this Notebook Trusted to load map: File -> Trust Notebook

    project.close()

Total running time of the script: (0 minutes 26.082 seconds)

Gallery generated by Sphinx-Gallery