Note
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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:
# Imports
from uuid import uuid4
from os import remove
from os.path import join
from tempfile import gettempdir
import folium
import geopandas as gpd
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()
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Now we will plot one of the route’s patterns we just imported
conn = database_connection("transit")
patterns = 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)
We turn the patterns and stops DataFrames into GeoDataFrames so we can plot them more easily.
patterns = gpd.GeoDataFrame(patterns, geometry=gpd.GeoSeries.from_wkt(patterns["geom"]), crs=4326)
stops = gpd.GeoDataFrame(stops, geometry=gpd.GeoSeries.from_xy(stops["X"], stops["Y"]), crs=4326)
And plot out data!
map = patterns.explore(color=["#146DB3", "#EB9719"], style_kwds={"weight": 4}, name="links")
map = stops.explore(m=map, color="black", style_kwds={"radius": 2, "fillOpacity": 1.0}, name="stops")
folium.LayerControl().add_to(map)
map
project.close()
This project at /tmp/b7a17710b9874cffbb71a6350e29d3c0 is already closed
Total running time of the script: (0 minutes 8.647 seconds)