General Transit Feed Specification Research Articles

Bus arrival and departure time updates in the Greater Sydney Area

Public Transport Authorities generate large quantities of data as part of their daily operations, including vehicle positions, arrival times, and dynamic routing options. This information is essential feedback into the system for planning routes and timetables, modelling passenger demand growth and evaluating operational performance. The General Transit Feed Specification (GTFS) is a data format that allows public transport data to be consumed by a wide variety of software applications. There are barriers to widespread consumption of GTFS data related to location-specific data extensions, non-human-readable formats and error cleaning. This paper describes a flexible dataset of actual bus arrivals and departure times created with a pipeline for GTFS realtime feeds designed to address these challenges. The paper describes the pipeline, verifies the quality of the data and presents an output of 25 months of actual bus arrival and departure times for Sydney, Australia. We conclude by discussing relevance to researchers and practitioners of the pipeline outputs in general and the sample data specifically.

Accessibility of methadone treatment via public transit for syringe services program participants in Miami-Dade County, Florida.

Methadone is an opioid receptor agonist medication used in the treatment of opioid use disorder (OUD). Geographic distance to opioid treatment programs (OTPs) is a major barrier to treatment, given requirements for direct observation of dosing and periodic drug screens, and 'methadone treatment deserts' are defined as a public transit threshold of 30 minutes. The purpose of this study was to examine public transit access to methadone treatment for participants of a syringe services program (SSP) in Miami-Dade County, Florida. Public transit times were calculated using the R library r5r, which facilitates multi-modal transportation network routing. General Transit Feed Specification data was combined with street network data from OpenStreetMap for Miami-Dade County. Transit times were estimated from the population-weighted centroid of each zip code (n=79) with participants of Miami's only SSP (n=1597) to the nearest OTP (n=4) using 10 departure windows aligned with OTP service hours. The mean one-way transit time from zip codes with SSP participants in Miami-Dade County to the nearest OTP was 80 minutes. 75 of the 79 (95%) zip codes with SSP participants in Miami-Dade County have a mean transit time to the closest OTP greater than 30 minutes. Transit times differ substantially between zip codes with different numbers of SSP participants, but not between departure windows. Nearly all zip codes with SSP participants in Miami-Dade County can be classified as 'methadone treatment deserts'. Geographic isolation of methadone treatment from public transit routes represents a significant barrier to equitable OUD treatment.

The public transport disadvantaged in a highly transit-oriented city: An analytical framework, key challenges and opportunities

This paper proposes a holistic framework to identify public transport disadvantaged areas in terms of the level of services (LOS), area-based accessibility, and facility-based accessibility by employing the General Transit Feed Specification (GTFS) dataset. The framework considers the spatial and temporal levels of transit services, the spatial separation by urban functional areas, the availability of facilities based on the cumulative opportunity method, and the minimum time cost based on dual accessibility. A case study is performed in Hong Kong. The results show that the main problem for rural residents using public transport services lies in walking to transit stops/piers instead of long waits at public transport stops or infrequent services. The map of area-based accessibility shows great variations by urban functional areas. Despite being a highly transit-dependent city, we find huge disparities of accessibility to public facilities within Hong Kong and people living in some rural areas have difficulties in accessing basic services such as education, health care and shopping through public transport. Theoretically, this study reinforces the significance of place-based transport disadvantage and its intricate relationship with urban form. Empirically, it provides practical recommendations for public transport planning. The findings can direct transit agencies to re-plan and deploy services such as introducing demand-responsive transit services to better address the needs of people living in transport disadvantaged areas.

Transit Board Diversity and Pandemic Service Cuts in Vulnerable Communities

Despite its primary role in shaping policy and service characteristics, board governance is an understudied topic in the field of urban transit. Existing research on board management and representative bureaucracy theory suggests that the race and gender diversity of boards has a significant impact on organizational activity but that these relationships are highly dependent on the cultural context and industry analyzed. In this paper, we evaluate how the diversity of transit boards (with respect to race, gender, and disability) in the U.S.A. correlates with service changes authorized by these boards during the COVID-19 pandemic. Utilizing a database on board governance and general transit feed specification data for 36 agencies, we find a positive relationship between the presence of women on transit boards and vertically equitable service cuts, defined as increasing or maintaining transit service in more vulnerable neighborhoods. Overall, transit agencies with more female board members had more equitable service cuts, on average, during the COVID-19 pandemic.

Origin–destination matrix estimation for public transport: A multi-modal weighted graph approach

Estimating the large-scale Origin–Destination (OD) matrices for multi-modal public transport (PT) in different cities can vary largely based on the network itself, what modes exist, and what traffic data is available. In this study, to overcome the issue of traffic data unavailability and effectively estimate the demand matrix, we employ several data sets like the total boarding and alighting, smart card as well as the General Transit Feed Specification (GTFS) in order to capture the PT dynamic patronage patterns.First, we propose a new method to model the dynamic large-scale stop-by-stop OD matrix for PT networks by developing a new enhancement of the Gravity Model via graph theory and Shannon’s entropy. Second, we introduce a method entitled “Entropy-weighted Ensemble Cost Features” that incorporates diverse sources of costs extracted from traffic states and the topological information in the network, scaled appropriately. Last, we compare the efficiency of a single travel cost versus various combinations of travel costs when using traditional methods like the Traverse Searching and the Hyman’s method, alongside our proposed “Entropy-weighted” method; we demonstrate the advantages of using topological features as travel costs and prove that our method, coupled with multi-modal PT OD matrix modelling, is superior to traditional methods in improving estimation accuracy, as evidenced by lower MAE, MAPE and RMSE, and reducing computing time.

Spillover effects in transit networks: A parameterized weight matrix spatial lagged approach

Public Transit (PT) systems aim to provide social, economic, and environmental benefits to modern cities offering reliable transportation service to users and ultimately reducing the problems related to traffic externalities due to car dependency. With the growing trend of urbanization and the associated phenomenon of cities’ sprawl being increasingly evident, significant attention should be given to understanding PT systems’ performance and then improving their efficiency. Considering the spatial characteristics of the PT system's performance promotes the environmentally friendly transport “character” that every city endeavors. This paper aims to incorporate the spatial spillover effects of a realistic PT system by augmenting information about the service network along with socio-economic variables, in a spatial demand-supply econometric framework. In detail, geographically separated demand and supply information on bus stops and lines was analyzed by a spatial econometric model, namely, the Spatial Lagged X (SLX) model which may be formatted so that can soundly handle, spatial and –in particular- network data like those coming from the General Transit Feed Specification (GTFS) protocol widely used in PT systems. The novelty and the importance of the proposed model rely on the ability to introduce transit network structure within the framework of spatial econometric regression, fostering the explanatory statistical analysis over networked information. The developed modeling approach was applied to the urban and rural PT system of Nicosia (Cyprus), incorporating the spatial spillover effects of the system over more than 1,500 bus stops, 40 lines, and 252 different but spatially connected postcode areas. The results of the SLX model were compared with other demand models of this form typically used, namely the Ordinary Least Square model and standard Spatial Autoregressive models, providing solid evidence of the benefits of incorporating the network structure in spatial demand modeling, giving valuable input for further planning purposes.

A Survey of Errors in GTFS Static Feeds from the United States

This study surveys the errors in General Transit Feed Specification (GTFS) Static (Schedule) data for 632 US transit feeds. We do so using the Canonical GTFS Schedule Validator tool provided by Mobility Data, which checks feeds against GTFS standards. About 21% of GTFS feeds have at least one error. We explain what the most common errors are and provide examples. Errors related to the optional shape_dist_traveled field account for the majority of errors. Fares account for a second cluster of errors. Manual investigation can reveal errors not captured programmatically.

A parallel set-based model on the shortest travel time in long-distance transportation systems

The shortest travel time in long-distance transportation systems (LDTS) is a key indicator for measuring regional connectivity and mapping accessibility at national and global levels. For traditional methods, it is a great challenge to calculate the shortest travel time with millions of origin-destination pairs and evaluate the overall performance. To fill this technique gap, this study proposed a novel model to get all the trips with different numbers of transfers through a series of set-based methods and then calculate all the shortest travel time between stops. The set-based model was tested for calculating the shortest travel time in LDTS of China. Using the optimization algorithm, we can significantly reduce the number of transfer trips that need to be calculated. For instance, the number of transfer trips decreased by 96.17%, 98.15%, and 79.02%, for conventional railway, high-speed railway, and air transportation, respectively, when the number of transfers was two. The set-based model can be extended to calculate the door-to-door travel time between places, for instance, mapping the fine-scale accessibility at the national level. Furthermore, we proved that this set-based model, proposed in this study, could also be parallelized and applied to any other LDTS in the General Transit Feed Specification format.

Evaluation Framework for Multi-Modal Public Transport Systems Based on Connectivity and Transfers at Stop Level

Multi-modal public transport (PT) networks within metropolitan areas are often characterized by complexity resulting mainly from their infrastructure, design, operations, and demand. This complexity leads to a significant amount of effort on behalf of the transit agencies to properly evaluate their performance at certain locations and proceed with improvements. This study proposes a methodology based on clustering techniques that facilitates the evaluation of PT networks. The evaluation framework refers to the comparison between the levels of supply and demand at a certain stop. Service supply is quantified through an existing connectivity index, whereas demand is considered through the number of transfers that are performed at each stop. Transfers are critical within multi-modal mobility and often serve as a hindrance for choosing PT. The case study here is the Helsinki PT network in Finland. General Transit Feed Specification (GTFS) data are used for quantifying connectivity and a dataset deriving from smartphone ticketing application for quantifying transfers. Results include the evaluation for each PT mode and for the overall multi-modal PT network. Focusing on the evaluation of the overall multi-modal PT network, connectivity and transfers levels for 75.60% of stops are found to be well aligned. Therefore, these stops could be eliminated from the list of candidate stops for performing improvements. Of the remaining stops, 19.73% belongs to the case of higher connectivity than transfers and 4.67% to the case of lower connectivity than transfers. Stops included in these two cases require further attention and prioritization during planning processes.

Bus stop spacing statistics: Theory and evidence

Discussions of bus stop consolidation sometimes refer to average stop spacings, but there are no reliable statistics about spacings, nor methodologies for calculating them. This paper aims to clarify discussions of bus stop spacings by introducing clear definitions, a methodology for creating statistics from General Transit Feed Specification (GTFS) files, and a python package, gtfs-segments, which splits bus networks into isolated ‘segments.’ With the package, we calculate national-level statistics from 539 US transit providers and 83 Canadian providers, as well as agency-level statistics for 30 providers in the US, 10 in Canada, and a sample of 38 providers from other countries. Our estimates of US and Canadian mean spacings are both around 350 m (slightly wider than five stops per mile). US spacings are wider than sometimes claimed but narrower than those in other countries. Finally, the paper gives examples of metrics created by combining GTFS with data from other sources and proposes research ideas and applications to transit planning involving fine-grained stop spacing data.

The potential of combining passenger rail with freight: A New York City case study

Utilizing passenger rail, including subways, to transport goods can have advantages over trucking in terms of efficiency and emissions. While some experimentation is ongoing in this area, combined passenger and rail opportunities in specific cities merit further attention. To more concretely examine how to leverage passenger rail for freight, this paper explores the potential of utilizing unused capacity in New York City (NYC) subway trains for transporting goods. Using General Transit Feed Specification (GTFS), rail and passenger data for the NYC subway, we found ways to use excess capacity and existing rail lines for freight transportation. We visualized a freight train timetable and graph, analyzed meet-errors between freight and passenger trains, and formulated prevention policies. Using U.S. Environmental Protection Agency data, we estimated emission reductions from replacing trucks with unused subway capacity. Our findings suggest that with adequate policies, investments and redesign, combining passenger rai and freight in the subways could significantly reduce truck trips, traffic congestion, and greenhouse gas emissions compared to trucking alone. However, implementing this approach requires careful collaborative planning, investment, enhanced security screening, and streamlined operations to minimize impacts on passenger transport. Further efforts should explore in more depth the costs and benefits and practical design and policy issues around using excess passenger rail capacity for freight transport in NYC and other cities.

Quantification and comparison of hierarchy in Public Transport Networks

Network hierarchy describes the relative arrangement of network elements and reflects its fundamental structure. We propose a multi-dimensional topology-based method for quantifying and comparing the extent to which different Public Transport Networks (PTNs) exhibit a hierarchical structure. The proposed method considers the uneven distribution of node importance with different definitions (e.g., degree centrality and betweenness centrality) in a PTN, the clustering of nodes and the node connection patterns. We apply the developed method on 63 high-capacity PTNs worldwide using General Transit Feed Specification (GTFS) data. In addition to global indicators, we use the goodness-of-fit between the probability density function of local indicators and a skew-normal distribution to quantify the extent of PTN hierarchy. Results show that the scale-free network structure and preferential attachment do not vary much across PTNs. In contrast, stop accessibility and traffic intermediacy vary considerably across PTNs as reflected by the closeness centrality and betweenness centrality distributions. Lastly, metro systems exhibit a more hierarchical structure than their tram and Bus Rapid Transit (BRT) counterparts. This work makes a first step towards a better mapping and comparison of different PTNs, which can assist academics and practitioners in better (re)designing and planning the PTNs of the future.

Measuring the impacts of disruptions on public transit accessibility and reliability

Public transit systems are facing higher risk of system degradation from external disruptions, affecting their ability to deliver reliable accessibility to transit users. Therefore, resilience, the ability to maintain functions during a disruption, becomes a crucial assessment of public transit systems. In this paper, we calculate two space-time prism-based measures with General Transit Feed Specification real-time (GTFS-RT) data: realizable real-time accessibility, a conservative real-time accessibility measure that can be achieved by users subject to delays, and scheduled accessibility, accessibility based on schedule. We also define accessibility unreliability, the deviation between realizable accessibility and scheduled accessibility, to measure the reliability of delivered accessibility. We use the two measures to gauge the resilience of public transit systems and conduct two case studies of short- and long-term disruptions, namely Ohio State football games and the COVID-19 pandemic, on the Central Ohio Transit Authority (COTA) bus system in Columbus, Ohio. We find there are two peaks of high unreliability before and after each football games, with the stadium as the geographic center of the disruption. The after-game peaks are shorter and more intense than the before-game. We also find COVID-19 had persistent negative impacts on accessibility and reliability: Realizable accessibility universally declined during the pandemic, but only part of cities experienced unreliability increase, primarily in urban perimeters and suburbs. Improved traffic conditions during the pandemic may help to reduce unreliability, but the later service cuts increased unreliability. The two case studies prove the effectiveness of the method to detect system disturbances and provide important guidance for public transit system operation and planning.

Transport and Mobility Segregation in Urban Spaces

Introduction & BackgroundPublic transportation is one of many factors that influence the level of disadvantage in a city. By facilitating movement within urban areas, transit systems can democratise accessibility to resources, while also fostering social integration among individuals from different areas and sociodemographic backgrounds. Conversely, inequalities in transport services can hinder individuals from fulfilling their travel demands. In this work, we explore socioeconomic segregation in cities from the perspective of their transit systems and how it intersects with segregation levels on a residential and employment level.
 Objectives & ApproachIn our analyses, we combine socioeconomic data from the 2020 American Community Survey with amenity visitation patterns from anonymised mobile phone traces, provided by SafeGraph, to estimate the mobility flows between areas (i.e., Census Block Groups - CBGs) in a given city. We define a CBG's segregation level using the Index of Concentration at the Extremes, which ranges from -1 to 1, reflecting extreme concentration of individuals from low and high income groups, respectively. Moreover, we retrieve General Transit Feed Specification and OpenStreetMap data to construct transit-pedestrian networks for various US cities.
 Relevance to Digital FootprintsWe leverage digital footprints, in the form of mobility flows between CBGs, to estimate the socioeconomic composition of different public transport routes within a city. By combining digital footprints with the respective economic breakdowns of trip origins, and transit-pedestrian networks, we can develop a better understanding of how segregated individuals are throughout various contexts of urban life.
 ResultsWhile segregation still exists in the transport and amenity dimensions, our findings suggest that individuals are exposed to the highest magnitudes of segregation in the residential dimension, with amenity and transit segregation allowing for potential avenues for reducing experiential segregation. However, we observe that the transit service in many cities hinders individuals in low-income neighbourhoods from accessing areas characterised by more affluent socioeconomic backgrounds.
 Conclusions & ImplicationsThese results underscore research that reveals how mobility patterns in neighbourhoods with a high concentration of underprivileged demographics, be it immigrant or ethnic minorities, tend to have more constrained activity spaces than their privileged counterparts. Although it is unclear whether mobility patterns are influenced by segregation levels of neighbourhoods, it is apparent that by limiting exposure to different types of neighbourhoods, transit systems impose constraints on the activity space and urban experience of individuals, namely those without access to personal vehicles. We highlight the benefit of analysing segregation as a spatio-temporal experience rather than a static variable, showing how mobility is used as a tool to try and overcome residential segregation. Moreover, identifying inequalities within transit systems is the first step in providing improved transit service, particularly to individuals from especially vulnerable demographics. Ultimately, by identifying how transit infrastructure may perpetuate segregation, we pursue the first of many steps to re-imagining transport as a point of inclusion within the urban realm.

Breaking down public transit travel time for more accurate transport equity policies: A trip component approach

In the Northern hemisphere, low-income transit users typically take shorter trips and use buses in a greater proportion, but there is limited knowledge on inequalities in the detailed characteristics of public transit trips.In this paper, we seek to understand and explain differences in segment components of transit trips across socio-economic groups. Trip components include access time, wait time, total travel time, overall trip speed and percent of trip time spent in access and wait (off-vehicle time).Using the Montreal metropolitan region travel survey (2013), we focus on the 46,186 transit trips made by 20,138 individuals for which household income was available. This data is augmented by time estimations of the aforementioned trip components, and combined with trips specific transit service measures calculated using General Transit Feed Specification (GTFS). We first assess the relationship between trip components and income using descriptive statistics. OLS regressions are then conducted to identify which individual and transport-based factors contribute to variations in trip component outcomes.Low-income transit users take more transit trips (especially bus and Metro) than their higher income counterparts and combine multiple transit modes through more frequent transfers. While average access and wait time are individually lower than those for trips of wealthier respondents, low-income total trip time is similar but trip speed is considerably lower. Lower-income populations also spend a greater share of their trips in access and wait time due to service and trip discrepancies including trip timing, proximity to metro stations and requirement for transfers. When controlling for other factors, the conditions under which trips by low-income individuals are taken mainly drive income differences.

Simplified geodata models for integrated urban and public transport planning

Abstract. The current division between urban and transport planning is a significant obstacle to achieving sustainable urban development. To transform cities towards sustainability, both fields must adopt shared or at least compatible models of the urban systems, namely transport, street and public space networks for all users and urban activities. Although several models and tools have emerged in recent years to facilitate this integration, there are still usability gaps that hinder their wider adoption. One of the gaps is a lack of flexibility to operate at different stages of integrated planning. To address this gap, the study aims to develop a set of aligned and flexible multimodal urban network models and tools to support different stages of planning. This paper focuses on the public transport geodata models, which were built by aggregating a General Transit Feed Specification (GTFS) dataset at various spatial and temporal levels. The aggregation levels range from a baseline data model that is useful for detailed planning stages, up to a topological data model that is suitable for macro scale and strategic planning. By using this unified set of models, the dialogue between the two fields at different integrated planning phases can be facilitated, and decision-making can be enhanced.

Estimating public transport emissions from General Transit Feed Specification data

This paper introduces the gtfs2emis model, a bottom-up method available as an R package to estimate emissions of public transport systems. The method uses General Transit Feed Specification (GTFS) data, a standard format for public transport data widely adopted worldwide, which makes the method easily applicable to cities with limited data. The model requires a GTFS feed of a given transport system and a table with general characteristics of the vehicle fleet profile. The package can estimate over 16 pollutants and energy consumption based on emission factor models from Europe, the United States, and Brazil. It also includes functions to help users examine how emissions are distributed across space, at different times of the day, and by types of vehicles. This paper presents a reproducible example of the city of São Paulo (Brazil) to demonstrate the gtfs2emis package and to discuss the potential applications and limitations of the proposed model.

Gtfs2net: Extraction of General Transit Feed Specification Data Sets to Abstract Networks and Their Analysis

Mass transportation networks of cities or regions are interesting and important to be studied to get a picture of the properties of a somehow better topology and system of transportation. One way to do this lies on the basis of spatial information of stations and routes. As we show however interesting findings can be gained also if one studies the abstract network topologies of these systems. To get these abstract types of networks, we have developed a tool that can extract a network of connected stops from General Transit Feed Specification feeds. As we found during the development, service providers do not follow the specification in coherent ways, so as a kind of postprocessing we have introduced virtual stations to the abstract networks that gather close stops together. We analyze the effect of these new stations on the abstract map as well.

Applying the LOT Methodology to a Public Bus Transport Ontology aligned with Transmodel: Challenges and Results

We present an ontology that describes the domain of Public Transport by bus, which is common in cities around the world. This ontology is aligned to Transmodel, a reference model which is available as a UML specification and which was developed to foster interoperability of data about transport systems across Europe. The alignment with this non-ontological resource required the adaptation of the Linked Open Terms (LOT) methodology, which has been used by our team as the methodological framework for the development of many ontologies used for the publication of open city data. The ontology is structured into three main modules: (1) agencies, operators and the lines that they manage, (2) lines, routes, stops and journey patterns, and (3) planned vehicle journeys with their timetables and service calendars. Besides reusing Transmodel concepts, the ontology also reuses common ontology design patterns from GeoSPARQL and the SOSA ontology. As part of the LOT data-driven validation stage, RDF data has been generated taking as input the GTFS feeds (General Transit Feed Specification) provided by the Madrid public bus transport provider (EMT). Mapping rules from structured data sources to RDF were developed using the RDF Mapping Language (RML) to generate RDF data, and queries corresponding to competency questions were tested.

Transit Arrival Time Prediction Using Interaction Networks

Accurate transit arrival time prediction is a critical factor for improving the quality of transit services. A significant factor that can affect transit arrival times is the other vehicles traveling on transit routes, as transit arrival time is significantly affected by traffic conditions. However, few previous studies considered traffic condition impacts in transit arrival time prediction due to a lack of real-time traffic data, reducing the accuracy in predictions under varying traffic conditions. To fill this research gap, crowdsourced speed data with wide coverage is utilized to indicate traffic conditions and to predict transit arrival time in conjunction with General Transit Feed Specification (GTFS) data. Interaction Networks (INs) are employed to model the interactions between transit speed, dwell time, and traffic speed for arrival time prediction. The proposed method only uses limited historical data and can predict arrival time at stops along a transit route. Ten days of data were collected from bus route #4 in Tucson, Arizona to evaluate the performance of the proposed method. The evaluation results show the average mean absolute percentage error (MAPE) of predicted arrival time on weekdays and weekends is 13.5% and 14%, respectively, indicating that the proposed method is promising for predicting transit arrival time while considering real-time traffic conditions. Furthermore, six traditional methods are compared with the proposed method, and the comparison results show the proposed method outperforms the other six methods.