gtfs-functions 2.0.4

Package to easily wrangle GTFS files geospatially.

GTFS functions

This package allows you to create various layers directly from the GTFS and visualize the results in the most straightforward way possible.

Update March 2023:

• Removed dependency with partridge. As much as we love this package and think it is absolutely great, removing a dependency gives us more control and keeps this package from failing whenever something changes in partridge.
• We treat the GTFS as a class, where each file is a property. See examples below to find out how to work with it. We hope this simplifies your code.
• Fixed and enhanced segment cutting. Shout out to Mattijs De Paepe
• Support to identify route patterns!! Check it out using feed.routes_patterns. Shout out to Tobias Bartsch
• The rest should stay the same.

Warning!

Make sure stop_times.txt has no Null values in the columns arrival_time and departure_time. If this is not the case, some functions on this package might fail.

Table of contents

• Installation
• GTFS parsing
• Stop frequencies
• Line frequencies
• Cut in Bus segments
• Speeds
• Segment frequencies
• Mapping the results
• Other plots

Python version

The package requires python>=3.8. You can create a new environment with this version using conda:

conda create -n new-env python=3.8

Installation

You can install the package running the following in your console:

pip install gtfs_functions==2.0.3

Import the package in your script/notebook

from gtfs_functions import Feed, map_gdf

GTFS Import

Now you can interact with your GTFS with the class Feed. Take a look at the class with ?Feed to check what arguments you can specify.

gtfs_path = 'data/sfmta.zip'

# It also works with URL's
gtfs_path = 'https://transitfeeds.com/p/sfmta/60/latest/download'

feed = Feed(gtfs_path, time_windows=[0, 6, 10, 12, 16, 19, 24])

routes = feed.routes
routes.head(2)

	route_id	agency_id	route_short_name	route_long_name	route_desc	route_type	route_url	route_color	route_text_color
0	15761	SFMTA	1	CALIFORNIA		3	https://SFMTA.com/1
1	15766	SFMTA	5	FULTON		3	https://SFMTA.com/5

stops = feed.stops
stops.head(2)

	stop_id	stop_code	stop_name	stop_desc	zone_id	stop_url	geometry
0	390	10390	19th Avenue & Holloway St				POINT (-122.47510 37.72119)
1	3016	13016	3rd St & 4th St				POINT (-122.38979 37.77262)

stop_times = feed.stop_times
stop_times.head(2)

	trip_id	arrival_time	departure_time	stop_id	stop_sequence	stop_headsign	pickup_type	drop_off_type	shape_dist_traveled	route_id	service_id	direction_id	shape_id	stop_code	stop_name	stop_desc	zone_id	stop_url	geometry
0	9413147	81840.0	81840.0	4015	1		NaN		NaN	15761	1	0	179928	14015	Clay St & Drumm St				POINT (-122.39682 37.79544)
1	9413147	81902.0	81902.0	6294	2		NaN		NaN	15761	1	0	179928	16294	Sacramento St & Davis St				POINT (-122.39761 37.79450)

trips = feed.trips
trips.head(2)

	trip_id	route_id	service_id	direction_id	shape_id
0	9547346	15804	1	0	180140
1	9547345	15804	1	0	180140

shapes = feed.shapes
shapes.head(2)

	shape_id	geometry
0	179928	LINESTRING (-122.39697 37.79544, -122.39678 37...
1	179929	LINESTRING (-122.39697 37.79544, -122.39678 37...

Stop frequencies

Returns a geodataframe with the frequency for each combination of stop, time of day and direction. Each row with a Point geometry. The user can optionally specify cutoffs as a list in case the default is not good. These cutoffs should be specified at the moment of reading the Feed class. These cutoffs are the times of days to use as aggregation.

time_windows = [0, 6, 9, 15.5, 19, 22, 24]

feed = Feed(gtfs_path, time_windows=time_windows)
stop_freq = feed.stops_freq
stop_freq.head(2)

	stop_id	dir_id	window	ntrips	min_per_trip	stop_name	geometry
8157	5763	Inbound	0:00-6:00	1	360	Noriega St & 48th Ave	POINT (-122.50785 37.75293)
13102	7982	Outbound	0:00-6:00	1	360	Moscow St & RussiaAvet	POINT (-122.42996 37.71804)
9539	6113	Inbound	0:00-6:00	1	360	Portola Dr & Laguna Honda Blvd	POINT (-122.45526 37.74310)
12654	7719	Inbound	0:00-6:00	1	360	Middle Point & Acacia	POINT (-122.37952 37.73707)
9553	6116	Inbound	0:00-6:00	1	360	Portola Dr & San Pablo Ave	POINT (-122.46107 37.74040)

Line frequencies

Returns a geodataframe with the frequency for each combination of line, time of day and direction. Each row with a LineString geometry. The user can optionally specify cutoffs as a list in case the default is not good. These cutoffs should be specified at the moment of reading the Feed class. These cutoffs are the times of days to use as aggregation.

line_freq = feed.lines_freq
line_freq.head()

	route_id	route_name	dir_id	window	min_per_trip	ntrips	geometry
376	15808	44 O'SHAUGHNESSY	Inbound	0:00-6:00	360	1	LINESTRING (-122.46459 37.78500, -122.46352 37...
378	15808	44 O'SHAUGHNESSY	Inbound	0:00-6:00	360	1	LINESTRING (-122.43416 37.73355, -122.43299 37...
242	15787	25 TREASURE ISLAND	Inbound	0:00-6:00	360	1	LINESTRING (-122.39611 37.79013, -122.39603 37...
451	15814	54 FELTON	Inbound	0:00-6:00	360	1	LINESTRING (-122.38845 37.73994, -122.38844 37...
241	15787	25 TREASURE ISLAND	Inbound	0:00-6:00	360	1	LINESTRING (-122.39542 37.78978, -122.39563 37...

Bus segments

Returns a geodataframe where each segment is a row and has a LineString geometry.

segments_gdf = feed.segments
segments_gdf.head(2)

	route_id	direction_id	stop_sequence	start_stop_name	end_stop_name	start_stop_id	end_stop_id	segment_id	shape_id	geometry	distance_m
0	15761	0	1	Clay St & Drumm St	Sacramento St & Davis St	4015	6294	4015-6294	179928	LINESTRING (-122.39697 37.79544, -122.39678 37...	205.281653
1	15761	0	2	Sacramento St & Davis St	Sacramento St & Battery St	6294	6290	6294-6290	179928	LINESTRING (-122.39761 37.79446, -122.39781 37...	238.047505

Scheduled Speeds

Returns a geodataframe with the speed_kmh for each combination of route, segment, time of day and direction. Each row with a LineString geometry. The user can optionally specify cutoffs as explained in previous sections.

# Cutoffs to make get hourly values
speeds = feed.avg_speeds
speeds.head(1)

	route_id	route_name	direction_id	segment_id	window	speed_kmh	start_stop_id	start_stop_name	end_stop_id	end_stop_name	distance_m	stop_sequence	runtime_sec	segment_max_speed_kmh	geometry
0	15761	1 CALIFORNIA	Inbound	4015-6294	10:00-11:00	12.0	4015	Clay St & Drumm St	6294	Sacramento St & Davis St	205.281653	1	61.9	12.0	LINESTRING (-122.39697 37.79544, -122.39678 37...

Segment frequencies

segments_freq = feed.segments_freq
seg_freq.head(2)

	route_id	route_name	direction_id	segment_name	window	min_per_trip	ntrips	start_stop_id	start_stop_name	end_stop_name	geometry
23191	ALL_LINES	All lines	NA	3628-3622	0:00-6:00	360	1	3628	Alemany Blvd & St Charles Ave	Alemany Blvd & Arch St	LINESTRING (-122.46949 37.71045, -122.46941 37...
6160	15787	25 TREASURE ISLAND	Inbound	7948-8017	0:00-6:00	360	1	7948	Transit Center Bay 29	Shoreline Access Road	LINESTRING (-122.39611 37.79013, -122.39603 37...

Map your work

Stop frequencies

# Stops
condition_dir = stop_freq.dir_id == 'Inbound'
condition_window = stop_freq.window == '6:00-9:00'

gdf = stop_freq.loc[(condition_dir & condition_window),:].reset_index()

gtfs.map_gdf(gdf = gdf, 
              variable = 'ntrips', 
              colors = ["#d13870", "#e895b3" ,'#55d992', '#3ab071', '#0e8955','#066a40'], 
              tooltip_var = ['min_per_trip'] , 
              tooltip_labels = ['Frequency: '], 
              breaks = [10, 20, 30, 40, 120, 200])

Line frequencies

# Line frequencies
condition_dir = line_freq.direction_id == 'Inbound'
condition_window = line_freq.window == '6:00-9:00'

gdf = line_freq.loc[(condition_dir & condition_window),:].reset_index()

gtfs.map_gdf(gdf = gdf, 
              variable = 'ntrips', 
              colors = ["#d13870", "#e895b3" ,'#55d992', '#3ab071', '#0e8955','#066a40'], 
              tooltip_var = ['route_name'] , 
              tooltip_labels = ['Route: '], 
              breaks = [5, 10, 20, 50])

Speeds

If you are looking to visualize data at the segment level for all lines I recommend you go with something more powerful like kepler.gl (AKA my favorite data viz library). For example, to check the scheduled speeds per segment:

# Speeds
import keplergl as kp
m = kp.KeplerGl(data=dict(data=speeds, name='Speed Lines'), height=400)
m

Segment frequencies

# Segment frequencies
import keplergl as kp
m = kp.KeplerGl(data=dict(data=seg_freq, name='Segment frequency'), height=400)
m

Other plots

Histogram

# Histogram
import plotly.express as px
px.histogram(
    stop_freq.loc[stop_freq.min_per_trip<50], 
    x='frequency', 
    title='Stop frequencies',
    template='simple_white', 
    nbins =20)

Heatmap

# Heatmap
import plotly.graph_objects as go
dir_0 = speeds.loc[(speeds.dir_id=='Inbound')&(speeds.route_name=='1 CALIFORNIA')].sort_values(by='stop_sequence') 
dir_0['hour'] = dir_0.window.apply(lambda x: int(x.split(':')[0]))
dir_0.sort_values(by='hour', ascending=True, inplace=True)

fig = go.Figure(data=go.Heatmap(
                   z=dir_0.speed_kmh,
                   y=dir_0.start_stop_name,
                   x=dir_0.window,
                   hoverongaps = False,
                   colorscale=px.colors.colorbrewer.RdYlBu, 
                   reversescale=False
))

fig.update_yaxes(title_text='Stop', autorange='reversed')
fig.update_xaxes(title_text='Hour of day', side='top')
fig.update_layout(showlegend=False, height=600, width=1000,
                 title='Speed heatmap per direction and hour of the day')

fig.show()

Line chart

by_hour = speeds.pivot_table('speed_kmh', index = ['window'], aggfunc = ['mean','std'] ).reset_index()
by_hour.columns = ['_'.join(col).strip() for col in by_hour.columns.values]
by_hour['hour'] = by_hour.window_.apply(lambda x: int(x.split(':')[0]))
by_hour.sort_values(by='hour', ascending=True, inplace=True)

# Scatter
fig = px.line(by_hour, 
           x='window_', 
           y='mean_speed_kmh', 
           template='simple_white', 
           #error_y = 'std_speed_kmh'
                )

fig.update_yaxes(rangemode='tozero')

fig.show()