#WESTERNUSRI2021 CONFERENCE: RESEARCH OUTPUT SHOWCASE

 
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#WESTERNUSRI2021 CONFERENCE: RESEARCH OUTPUT SHOWCASE
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                          #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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                          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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                          Access or accessibility? A critique of the urban transport SDG
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                          off Locations in Texas. Transport Findings, May, 1–5.
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                          Geographer, 69(3), 362–371.
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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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