#WESTERNUSRI2021 CONFERENCE: RESEARCH OUTPUT SHOWCASE
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8/22/2021 #WesternUSRI2021 Conference: Research Output Showcase
#WesternUSRI2021
Conference: Research
Output Showcase
General Transit Feed Specification (GTFS) data in
transportation research: A review of applications and
methods
Stanley Ho
August 22, 2021
Introduction
(This is a printout of a Storymap that some interactive content
maybe only visible on the webpage, for the latest version please
visit here.)
This research falls under the discipline of Urban Geography/
Geographic Information Science (GIS). The objective of this research
is to sharing knowledge of a public transportation dataset named
General Transit Feed Specification (GTFS). Transportation research
using GTFS and other open source data can be easily reproduced in
many cities and to answer many urban geography questions that
involves public transportation. Readers can find that GTFS and
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8/22/2021 #WesternUSRI2021 Conference: Research Output Showcase
related research are conducted globally through the selected
collection of journal articles. The demonstration aims to showcase
that research using GTFS is feasible and easy to learn by public with
little amount of open data.
GTFS Brief and Demonstration
Brief History of GTFS
GTFS is developed by TriMet (a public transit agency from Portland,
Oregon) and Google in 2005, it was originally branded as Google
Transit Feed Specification for the purpose of visualizing the city’s
public transportation network and trip-planning by Google Maps
(McHugh, 2013). It is renamed in 2010 as General Transit Feed
Specification as this dataset format is not exclusively used by Google
products, where it can be used by third-party developers for trip
planning, creating timetable, data visualization, accessibility and
urban planning analysis, as well as real-time information systems
(GTFS.org, 2021b). GTFS is published and maintained by public
transit agencies voluntarily, where they are open, free, and
accessible to the public (OpenMobilityData, 2021; Transitland,
2021). The GTFS standard has gained popularity worldwide, where
the World Bank also advocates for it and provides resources to help
countries to adopt it internationally (Antrim et al., 2017).
Data Structure of GTFS
GTFS is a set of text files enclosed in a zipped folder that contains
necessary information for the purpose of routing. They are
subjected to update when agencies update their transit services due
to operational needs, such as seasonal changes or service cut during
the COVID-19 pandemic. GTFS dataset can be used offline (static)
or online (GTFS-Realtime, where available) for real-time service
information (GTFS.org, 2021a).
Below are the format of static GTFS:
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Required (Must be included in the
dataset.):
• agency.txt (Representation of transit
agencies in the dataset.)
Diagram Attribution: Martin Davis
• stops.txt (Stops for vehicles to pick up
or drop off riders. Definition for
stations and station entrances.)
• routes.txt (Transit routes, where a route is a group of trips
displayed as a single service.)
• trips.txt (Trips for each route, where a trip is a sequence of at
least two stops that occur during a specific time period.)
• stop_times.txt (Stops' arrival and departure time for a vehicle for
each trip.)
Conditionally Required (Required under certain conditions.):
• calender.txt (To specify service dates using a weekly schedule
with start and end dates. Required unless calendar_dates.txt has
defined all dates of services.)
• calender_dates.txt (Exceptions for the services defined in the
calendar.txt, such as holidays or scheduled reroute or downtime.
Required and must contain all dates of service if calendar.txt is
omitted.)
Optional (Can be omitted or empty strings in some field.):
• fare_attributes.txt (Fare information for a transit agency's
routes.)
• fare_rules.txt (Rules to apply fares for itineraries.)
• shapes.txt (Rules for mapping vehicle travel paths, sometimes
referred to as route alignments.)
• frequencies.txt (Headway (time between trips) for headway-
based service or a compressed representation of fixed-schedule
service.)
• transfers.txt (Rules for making connections at transfer points
between routes.)
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• pathways.txt (Pathways linking together locations within
stations.)
• levels.txt (Levels within stations.)
• translations.txt (Translations of customer-facing dataset values.)
• feed_info.txt (Dataset metadata, including publisher, version,
and expiration information.)
• attributions.txt (Dataset attributions.)
Case Study: Finding Bus Route to Mass Vaccination Clinics
in London, ON
The most well known usage of GTFS is for planning trip via public
transportation. For example, when a London resident is interested
in booking an appointment for COVID-19 vaccination through one
of the three mass vaccination clinics in the city. They can access
information of the suggested routes and time expectation by
inputting their origin, destination, desired departure/arrival time, as
well as preference like minimizing transfers, minimizing walking
distances, or accessibility needs for wheelchair users. As London
Transit provides Realtime vehicle positions, this can update riders
with the latest transit information and to make plans accordingly.
Google Transit trip planner. For a trip that the passenger wish to arrive by 3pm.
Demonstration on GIS Method and Application: How GTFS
can be used by Public Health Officials to study Transit
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Accessibilities of the Chosen Mass Vaccination Clinic
Locations.
But for public health officials (i.e. Middlesex London Health Unit/
London Health Sciences Centre), one of the possible GTFS usages
for them can be studying the transit-based accessibility of their
chosen clinics through a service area analysis. In the field of human
geography, accessibility is defined as the person's ability to access
economical and social opportunities in the area, where
opportunities can be anything like employment, healthcare services,
recreations, and other amenities (Wachs & Kumagai, 1973).
For this demonstration, we are specifically looking for residents who
are able to arrive any of the mass vaccination clinics from their
home within a 30-minute and 60-minute threshold by public transit
and walking only.
The GTFS dataset can be obtained from London Transit's website or
from OpenMobiltyData. Roads, sidewalks, zoning information is
obtained from the Open Data database from the City of London.
The map above showcase the service area of the Mass Vaccination
Clinics (Green = 30-min threshold; Yellow = 60-min threshold),
where residential areas are colored in purple. The assumption is
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that on Sunday, residents living within the threshold zones will be
able to arrive the clinics in the said time threshold if they leave at
2pm.
As we can see here many residential areas are not covered by the
clinics, which means they will spend more times to travel via public
transportation. At this moment, public health officials can make
decision to make the vaccine to be more accessible by strategies
like:
• Pop-up Clinics
• Participating pharmacies
• Mobility On Demand services
• Bus Mobile Clinics
Literature Review: A Global Study
20 journal articles have been reviewed to showcase that research
have been conducted globally using GTFS dataset. Highlights of the
studies can be read through selecting the point of interest on the
interactive map below.
Esri, GEBCO, DeLorme | Esri, FAO, NOAA Powered by Esri
(Jomehpour Chahar Aman & Smith-Colin, 2020)
City: Dallas, Texas
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(Karner & Rowangould, 2021)
City: Harris County, Texas
(Farber et al., 2014)
City: Cincinnati, Ohio
(Widener, 2017)
City: Hamilton County, Ohio
(Goliszek et al., 2020)
City: Szczecin, Poland
(Li & Dodson, 2020)
City: Melbourne, Australia
(Kim et al., 2021)
City: Seoul, Korea (1936)
(Eberhart et al., 2015)
City: Philadelphia, PA
(Madill et al., 2018)
City: Melbourne, Australia
(Hyder et al., 2021)
City: Franklin County, Ohio
(Pereira 2018)
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(Pereira, 2018)
City: Rio de Janeiro, Brazil
(Pereira, 2019)
City: Rio de Janeiro, Brazil
(Pritchard et al., 2019)
City: São Paulo, Brazil
(Slovic et al., 2019)
City: São Paulo, Brazil
(Williams et al., 2015)
City: Nairobi, Kenya
(Brussel et al., 2019)
City: Bogotá, Colombia
(Lee & Miller, 2018)
City: Columbus, Ohio
(Radzimski & Dzięcielski, 2021)
City: Poznań, Poland
(Sieber et al., 2020)
City : Switzerland (Homburgertal, Boncourt, Thunersee, Tösstal)
(Young et al., 2020)
City: Toronto, Ontario
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Discussion
For this part we will discuss on the themes for GTFS research.
1. Social Equity
We found that lots of research papers were discussing on the theme
of inequalities. Given that GTFS is a standardized, open dataset
widely used by cities around the world to showcase its public
transportation services, it makes measurements and comparison of
transit-based accessibility viable (Bok & Kwon, 2016). Some
research are focused on the inequalities between automobile users
and public transit users (Brussel et al., 2019; Eberhart et al., 2015;
Karner & Rowangould, 2021; Li & Dodson, 2020; Widener, 2017).
Some are focused on the disparities between neighbourhood
(Farber et al., 2014; Jomehpour Chahar Aman & Smith-Colin, 2020;
Kim et al., 2021; Madill et al., 2018).
On the other hand, the areas of research interest can be varied:
• Job Accessibility (Brussel et al., 2019; Goliszek et al., 2020;
Jomehpour Chahar Aman & Smith-Colin, 2020; Kim et al., 2021;
Lee & Miller, 2018; Li & Dodson, 2020; Pereira, 2019; Pritchard et
al., 2019; Slovic et al., 2019)
• Healthcare (Eberhart et al., 2015; Hyder et al., 2021; Madill et al.,
2018; Pereira, 2018)
• Food Security (Farber et al., 2014; Widener, 2017)
• Voting Rights (Karner & Rowangould, 2021)
2. Global South
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There are disparities in GTFS dataset availability, Where most of the
participating agencies are from the Global Norths (North America
and Europe) , meanwhile very few were from the Global Souths
(Central/South America and Africa) (Bok & Kwon, 2016). Since one
of the United Nation’s Sustainability Goals (SDG) is to increase the
accessibility of transit services in developing countries, it is
worthwhile to put focus into the implementation of GTFS and to
explore its potential to assist human development research (Brussel
et al., 2019; Slovic et al., 2019). One of the focus can be in studying
how transportation investment and planning can facilitate local
residents (Pereira, 2018, 2019). For planning, feasibility of
multimodal transportation can also be discussed such as improving
cycle networks to encourage more bike-and-ride behaviors instead
of driving (Pritchard et al., 2019). Also, GTFS dataset maybe
unavailable due to the nature of semi-formal transit system. By
developing methods to capture data necessary for building a GTFS
dataset through public participation, it is possible to overcome
technical barriers and to facilitate transportation research in the
Souths (Williams et al., 2015).
3. Future of Mobility
With technological advancement, public transportation may face
many challenges and opportunities. To retain and to increase
ridership, transport authorities need to investigate the latest
developments in mobility studies. In current settings, policy makers
from both developed and developing cities should be open-minded
to introduce Rapid Transit System into their transit network, given
that Rapid Transit can largely improve the performance of their
services, as well as reducing inequalities in their cities (Lee & Miller,
2018; Pereira, 2019). Secondly, improving connectivity, as known as
the first mile/last mile problem, can help to increase the utilization
of public transit and reduce road traffic by cars. Multimodal
transportation involving bike, walk, and transit is not applicable to
Global South only, it can also be adopted in Global Norths, where
there are potentials in integrating bike-sharing system into the city’s
transportation system, under the model of mobility-as-a-service
(MaaS) (Radzimski & Dzięcielski, 2021). For Mobility-on-Demand
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(MoD) or ride hailing services like Uber and Lyft, while it may be
competitive with public transport in some circumstances, focus can
be put on their complementary effects to improve the mobility
situation in areas that are remote and having low public transit
demand (i.e. suburbs), that public transport may be comparative
expensive and inefficient to operate (Young et al., 2020). With the
development of autonomous vehicles, the operation cost of
mobility-on-demand can be even lower as they can be operated
without drivers, making autonomous mobility-on-demand services
to be a feasible solution to solve mobility problems in rural areas
(Sieber et al., 2020).
Conclusion
We have discussed on the purpose of GTFS in public transport and
its value in transportation research. Our demonstration also
showcased that pressing urban questions can be translated into
research easily thanks to the availability of open data like GTFS and
map data. Our review collection serves as a glimpse of possibilities
that GTFS can offer to researchers. We hope that more cities can
open their data to the public, so it can help researchers, students
and citizen science enthusiasts to study and to improve their cities.
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Acknowledgement
This project output is funded by Undergraduate Student Research Internships
(USRI) Program of Western University. The author would like to thanks Dr.
Jinhyung Lee (Assistant Professor, Geography) for his supervision.
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