GTFS functions

This package allows you to create various layers directly from the GTFS and visualize the results in the most straightforward way possible. It is still in its testing face.

Installation

!pip install gtfs_functions

import gtfs_functions as gtfs

GTFS Import

The function import_gtfs takes the path or the zip file as argument and returns 5 dataframes/geodataframes.

routes, stops, stop_times, trips, shapes = gtfs.import_gtfs(r"C:\Users\santi\Desktop\Articles\SFMTA_GTFS.zip")

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.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.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.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.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

This function will create a geodataframe with the frequency for each combination of stop, time of day and direction. Each row with a Point geometry. The stops_freq function takes stop_times and stops created in the previous steps as arguments. The user can optionally specify cutoffs as a list in case the default is not good. These cutoffs are the times of days to use as aggregation.

cutoffs = [0,6,9,15.5,19,22,24]
stop_freq = gtfs.stops_freq(stop_times, stops, cutoffs = cutoffs)
stop_freq.head(2)

	stop_id	dir_id	window	ntrips	frequency	max_trips	max_freq	stop_name	geometry
8157	5763	Inbound	0:00-6:00	1	360	5	12	Noriega St & 48th Ave	POINT (-122.50785 37.75293)
13102	7982	Outbound	0:00-6:00	1	360	3	20	Moscow St & RussiaAvet	POINT (-122.42996 37.71804)
9539	6113	Inbound	0:00-6:00	1	360	5	12	Portola Dr & Laguna Honda Blvd	POINT (-122.45526 37.74310)
12654	7719	Inbound	0:00-6:00	1	360	5	12	Middle Point & Acacia	POINT (-122.37952 37.73707)
9553	6116	Inbound	0:00-6:00	1	360	5	12	Portola Dr & San Pablo Ave	POINT (-122.46107 37.74040)

Line frequencies

This function will create a geodataframe with the frequency for each combination of line, time of day and direction. Each row with a LineString geometry. The line_freq function takes stop_times, trips, shapes, routes created in the previous steps as arguments. The user can optionally specify cutoffs as a list in case the default is not good. These cutoffs are the times of days to use as aggregation.

cutoffs = [0,6,9,15.5,19,22,24]
line_freq = gtfs.lines_freq(stop_times, trips, shapes, routes, cutoffs = cutoffs)
line_freq.head()

	route_id	route_name	dir_id	window	frequency	ntrips	max_freq	max_trips	geometry
376	15808	44 O'SHAUGHNESSY	Inbound	0:00-6:00	360	1	12	5	LINESTRING (-122.46459 37.78500, -122.46352 37...
378	15808	44 O'SHAUGHNESSY	Inbound	0:00-6:00	360	1	12	5	LINESTRING (-122.43416 37.73355, -122.43299 37...
242	15787	25 TREASURE ISLAND	Inbound	0:00-6:00	360	1	15	4	LINESTRING (-122.39611 37.79013, -122.39603 37...
451	15814	54 FELTON	Inbound	0:00-6:00	360	1	20	3	LINESTRING (-122.38845 37.73994, -122.38844 37...
241	15787	25 TREASURE ISLAND	Inbound	0:00-6:00	360	1	15	4	LINESTRING (-122.39542 37.78978, -122.39563 37...

Bus segments

The function cut_gtfs takes stop_times, stops, and shapes created by import_gtfs as arguments and returns a geodataframe where each segment is a row and has a LineString geometry.

segments_gdf = gtfs.cut_gtfs(stop_times, stops, shapes)
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

This function will create a geodataframe with the speed_kmh and speed_mph for each combination of line, segment, time of day and direction. Each row with a LineString geometry. The function speeds_from_gtfs takes routes, stop_times and segments_gdf created in the previous steps as arguments. The user can optionally specify cutoffs as a list in case the default is not good. These cutoffs are the times of days to use as aggregation.

# Cutoffs to make get hourly values
cutoffs = list(range(24))
speeds = speeds_from_gtfs(routes, stop_times, segments_gdf, cutoffs = cutoffs)
speeds.head(1)

	route_id	route_name	dir_id	segment_id	window	speed_kmh	s_st_id	s_st_name	e_st_id	e_st_name	distance_m	stop_seq	runtime_h	max_kmh	geometry	speed_mph	max_mph
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	0.017222	12.0	LINESTRING (-122.39697 37.79544, -122.39678 37...	7.456452	7.456452

speeds.loc[(speeds.segment_id=='3114-3144')&(speeds.window=='0:00-6:00')]

	route_id	route_name	dir_id	segment_id	window	speed_kmh	s_st_id	s_st_name	e_st_id	e_st_name	distance_m	stop_seq	runtime_h	max_kmh	geometry	speed_mph	max_mph
11183	15792	30 STOCKTON	Inbound	3114-3144	0:00-6:00	12.8	3114	3rd St & Brannan St	3144	3rd St & Bryant St	373.952483	2	0.028565	13.0	LINESTRING (-122.39323 37.77923, -122.39431 37...	7.953549	8.077823
16862	15809	45 UNION-STOCKTON	Inbound	3114-3144	0:00-6:00	13.0	3114	3rd St & Brannan St	3144	3rd St & Bryant St	373.952483	2	0.027778	13.0	LINESTRING (-122.39323 37.77923, -122.39431 37...	8.077823	8.077823
19889	15831	91 3RD-19TH AVE OWL	Outbound	3114-3144	0:00-6:00	17.0	3114	3rd St & Brannan St	3144	3rd St & Bryant St	373.952483	56	0.021667	17.0	LINESTRING (-122.39323 37.77923, -122.39431 37...	10.563307	10.563307
22823	ALL_LINES	All lines	NA	3114-3144	0:00-6:00	15.2	3114	3rd St & Brannan St	3144	3rd St & Bryant St	373.952483	2	0.024511	NaN	LINESTRING (-122.39323 37.77923, -122.39431 37...	9.444839	NaN

Segment frequencies

cutoffs = [0,6,9,15.5,19,22,24]
seg_freq = gtfs.segments_freq(segments_gdf, stop_times, routes, cutoffs = cutoffs)
seg_freq.head(2)

	route_id	route_name	dir_id	segment_id	window	frequency	ntrips	s_st_id	s_st_name	e_st_name	max_freq	max_trips	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	20	18	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	15	4	LINESTRING (-122.39611 37.79013, -122.39603 37...

seg_freq.loc[(seg_freq.segment_id=='3114-3144')&(seg_freq.window=='0:00-6:00')]

	route_id	route_name	dir_id	segment_id	window	frequency	ntrips	s_st_id	s_st_name	e_st_name	max_freq	max_trips	geometry
10566	15809	45 UNION-STOCKTON	Inbound	3114-3144	0:00-6:00	120	3	3114	3rd St & Brannan St	3rd St & Bryant St	12	5	LINESTRING (-122.39323 37.77923, -122.39431 37...
7604	15792	30 STOCKTON	Inbound	3114-3144	0:00-6:00	60	6	3114	3rd St & Brannan St	3rd St & Bryant St	12	5	LINESTRING (-122.39323 37.77923, -122.39431 37...
13209	15831	91 3RD-19TH AVE OWL	Outbound	3114-3144	0:00-6:00	30	12	3114	3rd St & Brannan St	3rd St & Bryant St	30	2	LINESTRING (-122.39323 37.77923, -122.39431 37...
16580	ALL_LINES	All lines	NA	3114-3144	0:00-6:00	17	21	3114	3rd St & Brannan St	3rd St & Bryant St	5	59	LINESTRING (-122.39323 37.77923, -122.39431 37...

Save files

file_name = 'stop_frequencies'
gtfs.save_gdf(stop_frequencies_gdf, file_name, shapefile=True, geojson=True)

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 = ['frequency'] , 
              tooltip_labels = ['Frequency: '], 
              breaks = [10, 20, 30, 40, 120, 200])

Line frequencies

# Line frequencies
condition_dir = line_freq.dir_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.frequency<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_seq') 
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.s_st_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()

Fancy line chart

# Line graphs
import plotly.graph_objects as go
example2 = speeds.loc[(speeds.s_st_name=='Fillmore St & Bay St')&(speeds.route_name=='All lines')].sort_values(by='stop_seq') 
example2['hour'] = example2.window.apply(lambda x: int(x.split(':')[0]))
example2.sort_values(by='hour', ascending=True, inplace=True)

fig = go.Figure()

trace = go.Scatter(
    name='Speed',
    x=example2.hour, 
    y=example2.speed_kmh,
    mode='lines',
    line=dict(color='rgb(31, 119, 180)'),
    fillcolor='#F0F0F0',
    fill='tonexty',
    opacity = 0.5)


data = [trace]

layout = go.Layout(
    yaxis=dict(title='Average Speed (km/h)'),
    xaxis=dict(title='Hour of day'),
    title='Average Speed by hour of day in stop Fillmore St & Bay St',
    showlegend = False, template = 'simple_white')

fig = go.Figure(data=data, layout=layout)

# Get the labels in the X axis right
axes_labels = [] 
tickvals=example2.hour.unique()[::3][1:]

for i in range(0, len(tickvals)):
    label = str(tickvals[i]) + ':00'
    axes_labels.append(label)

fig.update_xaxes(
    ticktext=axes_labels,
    tickvals=tickvals
)

# Add vertical lines
y_max_value = example2.speed_kmh.max()

for i in range(0, len(tickvals)):
    fig.add_shape(
        # Line Vertical
        dict(
            type="line",
            x0=tickvals[i],
            y0=0,
            x1=tickvals[i],
            y1=y_max_value,
            line=dict(
                color="Grey",
                width=1
            )
        )
    )
    
# Labels in the edge values
for i in range(0, len(tickvals)):    
    y_value = example2.loc[example2.hour==tickvals[i], 'speed_kmh'].values[0].round(2)
    fig.add_annotation(
        x=tickvals[i],
        y=y_value,
        text=str(y_value),
    )
fig.update_annotations(dict(
            xref="x",
            yref="y",
            showarrow=True,
            arrowhead=0,
            ax=0,
            ay=-18
))

fig.update_yaxes(rangemode='tozero')

fig.show()

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
LICENSE.txt		LICENSE.txt
README.md		README.md
google_transit.zip		google_transit.zip
gtfs_viz.ipynb		gtfs_viz.ipynb
seg_freq.zip		seg_freq.zip
segments_gdf.zip		segments_gdf.zip
speeds.zip		speeds.zip
stop_freq.zip		stop_freq.zip

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GTFS functions

Table of contents

Installation

GTFS Import

Stop frequencies

Line frequencies

Bus segments

Scheduled Speeds

Segment frequencies

Save files

Map your work

Stop frequencies

Line frequencies

Speeds

Segment frequencies

Other plots

Histogram

Heatmap

Line chart

Fancy line chart

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DavidKurzIBK/GTFS_VIZ_Vienna

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GTFS functions

Table of contents

Installation

GTFS Import

Stop frequencies

Line frequencies

Bus segments

Scheduled Speeds

Segment frequencies

Save files

Map your work

Stop frequencies

Line frequencies

Speeds

Segment frequencies

Other plots

Histogram

Heatmap

Line chart

Fancy line chart

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