<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Deriving Transit Performance Metrics from GTFS Data</title>
      <link>https://trid.trb.org/View/2732356</link>
      <description><![CDATA[Transit agencies devote extensive resources to producing General Transit Feed Specification (GTFS) Schedule and Realtime data to power trip planning applications. In representing scheduled and actual service characteristics, these data offer a theoretical off-label use to generate metrics of transit service performance. This project seeks to create and test a set of standardized protocols for deriving and visualizing these performance metrics from raw GTFS feeds. These protocols would be established in such a way as to enable transit agencies, planning organizations, transportation researchers, transit advocates, and community-based organizations to easily implement them on any transit system with available GTFS schedule and GTFS realtime feeds. Specific metrics would look at common transit issues tied to schedule deviation – from on-time performance to bus bunching – but at a more granular spatial and temporal level than ever before possible. This detail, literally at the stop and segment level, is designed to enable more effective transit planning and advocacy.

The research would first collect a multi-day sample of GTFS schedule and realtime data from one or more transit agencies. This information would serve as the core data for the entire project. These data would be stored in a relational database that enables spatial analysis, such as PostGIS. The research would first design appropriate cleaning and aggregating protocols to prepare the data for performance analysis. A web-based tool, likely using D3, would be designed to allow a user to interact with the data to generate and visualize the transit performance metrics. The tool would allow fine-grained filtering by location and time period to enable detailed analysis of transit performance. A key feature of this approach is to allow interactivity with the data.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:26:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732356</guid>
    </item>
    <item>
      <title>General Transit Feed Specification (GTFS) Phase II Report</title>
      <link>https://trid.trb.org/View/2663125</link>
      <description><![CDATA[This report provides an overview of the National Park Service (NPS) General Transit Feed Specification (GTFS) Phase II Project. To better connect visitors to parks and improve trip planning capabilities, the NPS is interested in improving and seamlessly sharing transit information on third-party applications, such as Google Maps and Apple Maps, and NPS digital products, such as the NPS app and website. With parks turning to transit service to help manage congestion and expand visitor access, creating and sharing GTFS feeds is a cost-effective way to enhance operational efficiency by reducing staff time devoted to sharing transit information with visitors and directing traffic. This project aligns with the 2025 NPS National Transportation Strategy objective of improving and expanding trip planning tools. GTFS is the standardized and widely accepted method for transmitting transit information to third-party navigation applications. GTFS feeds can either be “static,” displaying a pre-determined, fixed schedule, or “realtime,” displaying live updates of transit vehicle positions and expected arrival times. Establishing GTFS feeds can help visitors make more informed travel decisions and further integrate NPS systems into the larger transit network. This project aimed to build upon the Phase I work by creating static GTFS feeds for high boarding transit systems, compiling GTFS realtime feeds, and providing recommendations for continuing and improving GTFS feed creation and maintenance.]]></description>
      <pubDate>Thu, 12 Feb 2026 08:52:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663125</guid>
    </item>
    <item>
      <title>TransitGPT: a generative AI-based framework for interacting with GTFS data using large language models</title>
      <link>https://trid.trb.org/View/2582945</link>
      <description><![CDATA[This paper introduces a framework that leverages Large Language Models (LLMs) to answer natural language queries about General Transit Feed Specification (GTFS) data. The framework is implemented in a chatbot called TransitGPT with open-source code. TransitGPT works by guiding LLMs to generate Python code that extracts and manipulates GTFS data relevant to a query, which is then executed on a server where the GTFS feed is stored. It can accomplish a wide range of tasks, including data retrieval, calculations and interactive visualizations, without requiring users to have extensive knowledge of GTFS or programming. The LLMs that produce the code are guided entirely by prompts, without fine-tuning or access to the actual GTFS feeds. We evaluate TransitGPT using GPT-4o and Claude-3.5-Sonnet LLMs on a benchmark dataset of 100 tasks, to demonstrate its effectiveness and versatility. The results show that TransitGPT can significantly enhance the accessibility and usability of transit data.]]></description>
      <pubDate>Tue, 02 Sep 2025 08:45:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582945</guid>
    </item>
    <item>
      <title>Implementation of the National Intercity Bus Atlas</title>
      <link>https://trid.trb.org/View/2577107</link>
      <description><![CDATA[This report documents the purposes and methods of implementing and maintaining the National Intercity Bus Atlas, an online interactive map and database of the nation’s intercity bus network. The report provides (1) a detailed account of data collection, training, and engagement activities used to instruct transportation service providers on the benefits of having their data on the Intercity Bus Atlas and (2) instructions on how to submit data using General Transit Feed Specification (GTFS), a user-facing platform for presenting transit schedule and route information. Further, the report documents potential applications of the Intercity Bus Atlas data in planning and policymaking. The report will be useful to state department of transportation (DOT) planners and intercity bus service providers seeking to develop and maintain the interactive online atlas of intercity bus transit services within the United States.]]></description>
      <pubDate>Mon, 21 Jul 2025 09:59:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577107</guid>
    </item>
    <item>
      <title>Advancing Rural Mobility Program: Stage 1 Implementation Report [supporting dataset]</title>
      <link>https://trid.trb.org/View/2570011</link>
      <description><![CDATA[This dataset contains transit data specification feeds for rural agencies that were involved in the Advancing Rural Mobility Project (ARMP) as well as supporting artifacts such as marketing materials, phtos from publicity events, and survey results.]]></description>
      <pubDate>Wed, 16 Jul 2025 19:48:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570011</guid>
    </item>
    <item>
      <title>Advancing Rural Mobility Program: Stage 1 Implementation Report</title>
      <link>https://trid.trb.org/View/2569843</link>
      <description><![CDATA[The Advancing Rural Mobility Program (ARMP) Stage 1 pilot, led by the Michigan Department of Transportation (MDOT), aimed to improve access to rural transit services by developing and deploying General Transit Feed Specification for Flexible services (GTFS-Flex) data and a public-facing trip planner, MiTripPlanner. Initially proposed with four pilot agencies, the project successfully expanded to include 15 rural transit providers across 19 counties. The pilot addressed a critical gap in rural mobility data by enabling demand-response services to be discoverable in trip-planning tools. Through four project phases— Discovery, Development, Initial Implementation, and Amendment—MDOT and its partners created GTFS-Flex feeds, launched MiTripPlanner, and developed a System Administration Portal to support long-term data maintenance and scalability. Stage 1 demonstrated that GTFS-Flex can be effectively implemented across diverse rural transit systems, laying the groundwork for statewide expansion. The project improved data accessibility, enhanced agency technical capacity, and provided valuable insights for scaling. These efforts position ARMP to significantly improve transit visibility, access, and connectivity in Michigan’s rural communities in Stage 2.]]></description>
      <pubDate>Wed, 16 Jul 2025 19:48:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569843</guid>
    </item>
    <item>
      <title>Advancing Rural Mobility: Michigan Public Transit Open Data Standards Program: Data Management Plan</title>
      <link>https://trid.trb.org/View/2570012</link>
      <description><![CDATA[The Advancing Rural Mobility: Michigan Public Transit Open Data Standards Program (Advancing Rural Mobility Program, ARMP) seeks to provide access to static and real-time public transit information and improve people’s abilities to plan and book transit trips in rural communities. Michigan Department of Transportation (MDOT) has partnered with HNTB and four rural public transit agencies to pilot this program. Static and real-time public transit travel information is commonly accessible in urban areas. Open standard data is largely absent in rural communities, limiting awareness of travel options and creating a barrier to access public transit. The ARMP seeks to address this gap in mobility information by using existing and emerging open standard data specifications that work with demand-response services to deliver static and real-time transit information in rural communities (such as General Transit Feed Specification for Flexible services (GTFS-Flex) and General On-Demand Feed Specification (GOFS)). Data feeds will be available for integration into common trip-planning tools that are available for free on smartphones and websites.]]></description>
      <pubDate>Wed, 16 Jul 2025 19:48:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570012</guid>
    </item>
    <item>
      <title>Evaluating Spatiotemporal Transit Accessibility Based on Gravity Model: A Novel Framework Using Big Data</title>
      <link>https://trid.trb.org/View/2533668</link>
      <description><![CDATA[Accessibility to public transportation is a crucial indicator of how effectively a city’s public transportation system and services are provided to the general public. To examine the accessibility of transit services and to evaluate their performance, it is essential to have precise estimations of travel times be- tween transit stops due to variability in transit travel time throughout the day, which can affect operational characteristics and may create gaps in accessibility. Several researchers have failed to recognize the potential of spatiotemporal variations on Public Transit Accessibility (PTA). To tackle these issues, the present research introduced a structure for computing the duration of journeys between two stops. This method employs automated information like Automated Vehicle Location (AVL) and the static data from the General Transit Feed Specification (GTFS) to determine travel times for each transit trip across various operational hours. Considering the travel time as a measure of impedance in Gravity Model for evaluating accessibility for the transit services of Surat City, India. Results suggested that better infrastructure of transit in the centre and inner fringe regions provides better connectivity across the city and less friction in terms of travel time. The study outcomes can help transit agencies identify areas for service improvement and allocation of resources.]]></description>
      <pubDate>Wed, 30 Apr 2025 16:59:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2533668</guid>
    </item>
    <item>
      <title>gtfs2net: Extraction of General Transit Feed Specification Data Sets to Abstract Networks and Their Analysis</title>
      <link>https://trid.trb.org/View/2521942</link>
      <description><![CDATA[The authors developed a method to derive public transit abstract networks from the General Transit Feed Specification (GTFS) datasets published by transit agencies. Studies of urban and regional public transit networks are important for envisioning improved transportation systems. The authors' approach used the spatial information of transit stations and routes. The authors show that useful findings can also be derived from geographic information systems (GIS) data from transit systems. To derive the abstract networks, the authors used GTFS datasets to extract a network of connected transit stops. The authors found that transit service providers do not follow GTFS coherently, so as a post-processing step they inserted virtual transit stations in the abstract networks, and analyzed the effect of these new stations on the abstract map.]]></description>
      <pubDate>Thu, 24 Apr 2025 09:07:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521942</guid>
    </item>
    <item>
      <title>Using GTFS-data to calculate the roadwork caused delays on public transport network</title>
      <link>https://trid.trb.org/View/2493082</link>
      <description><![CDATA[The reliability of public transport is a significant reason whether people choose it over other transport modes. Roadworks, i.e., mandatory road maintenance, can cause delays on vehicle journeys in parts of the network, thus generating missed transport connections. To sustain the reliability of the public transport system, it is important to understand effects these delays cause. This study explains a method that allows to model delay effects to the network using GTFS-data. The developed method allows a simple workflow to calculate how different delays on different locations affect the reachability of the areas. The method is tested in two case areas in Finland. Based on the case areas in this study, the results indicate that a 3-minute delay occurring for the departures from city centre only seldomly appears as a total travel time delay longer than the calculated delay. However, for some parts of the city that rely on transfer connections, even a 2-minute delay will result in missed connections, and thus, longer travel times.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2493082</guid>
    </item>
    <item>
      <title>Accessibility Via Public Transport through Gravity Models Based on Open Data</title>
      <link>https://trid.trb.org/View/2487379</link>
      <description><![CDATA[Efficient urban facility access is crucial for enhancing residents' well-being and quality of life. This study examines public transport accessibility to essential facilities in Rome, such as high schools and hospitals, using gravity models based on open data sources, like OpenStreetMap and GTFS. Two versions of the gravity model are compared: an origin constrained and a doubly constrained. The latter can consider the capacity of facilities as a strict constraint, offering thus a more realistic assessment of accessibility. The findings highlight the correlation of public transport quality and urban accessibility, showing how variations in public transport supply affect the weighted average time to reach services like healthcare, education, and leisure. This approach reveals specific zones needing improvement and provides a valuable tool for predicting the impact of future changes in the transport network with respect to social equity. An online platform has been developed to allow a readily available application of this methodology to boost active citizenship and proactive decision making.]]></description>
      <pubDate>Mon, 27 Jan 2025 08:55:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487379</guid>
    </item>
    <item>
      <title>G2Viz: an online tool for visualizing and analyzing a public transit system from GTFS data</title>
      <link>https://trid.trb.org/View/2483259</link>
      <description><![CDATA[Public transit agencies have amassed substantial data through on-board and off-board sensors over the years. While data collection was the primary focus, there is now a shift towards deriving actionable insights from this wealth of information. As data-driven decision making becomes increasingly vital, there is a growing need for effective ways to visualize and convey complex insights to decision makers. This study addresses this need by introducing G2Viz, a visualizer for public transit operations. The development process of G2Viz spans requirement gathering, planning, and design, encompassing software architecture, data models, user interfaces, and system components. Rigorous implementation and testing ensure the tool’s functionality and effectiveness. G2Viz, designed to dynamically visualize public transit operations using General Transit Feed Specification (GTFS) data, is a web application accessible globally via any web browser. Its open-source nature, robustness, and versatility facilitate communication among transit agencies, users, researchers, and city authorities. G2Viz empowers transit planners to make well-informed decisions about public transportation. (Access G2Viz at https://g2viz.citycontext.info).]]></description>
      <pubDate>Mon, 13 Jan 2025 09:14:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2483259</guid>
    </item>
    <item>
      <title>Using Multi-Modal Path-Specific Transit Trips in Transportation Social Sustainability Analysis: Case Study in Atlanta, GA [supporting dataset]</title>
      <link>https://trid.trb.org/View/2479818</link>
      <description><![CDATA[Abstract of the final report is stated below for reference:  A previous National Center for Sustainable Transportation (NCST) study examined pandemic-related changes in Metropolitan Atlanta Rapid Transit Authority (MARTA) transit system service and ridership in Atlanta, Georgia, and the combined effects on energy use and per-passenger energy use (Fan, et al., 2022). For that previous study, General Transit Feed Specification (GTFS) and the Automated Passenger Counter (APC) datasets were used to develop the transit network and derive distance and passenger load information within the TransitSim analytical framework. The research coupled ridership data with energy use and emission rates from MOVES-Matrix to assess how the changes in transit service and ridership affected energy use and emissions on a per passenger-mile basis. Research performed in this supplemental NCST study improved model algorithms to increase analytical efficiency and to integrate ridership demographics, so that energy use impacts could be assessed across demographic groups for use in social sustainability analysis. This report summarizes improvements that generated TransitSim 3.0 and provides a social sustainability modeling demonstration.]]></description>
      <pubDate>Mon, 30 Dec 2024 09:58:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2479818</guid>
    </item>
    <item>
      <title>Measurement and Prediction of Transit System Performance Using Probe Data Generated through DSRC and non-DSRC Technologies</title>
      <link>https://trid.trb.org/View/2449511</link>
      <description><![CDATA[This research explores the application of two different probe data standards to transit performance measurement. The first section chronicles the proposed and implemented transit uses of dedicated short-range communication (DSRC) technologies during the two decades between the standard’s emergence and its announced sunset. The research finds that, despite proposed applications across safety, operation, and information domains, DSRC never became embedded in transit operations. By contrast, the general transit feed specification (GTFS) standard with its real-time (RT) extension has been widely embraced and offers the potential to use the associated VehiclePosition messages as probe data to generate detailed transit performance metrics. The second section presents a method to decompose transit routes into segments (defined as the path between subsequent stops) and to impute three key time points for each segment: arrival time at the segment start, departure time from the segment start, and arrival time at the segment end. This method is applied to several days of GTFS-RT data from the Modesto Area Express to assess the accuracy of the imputation (in comparison to reported times from the TripUpdate messages) and, in the third and final section, to generate and visualize a series of performance metrics. These metrics assess the deviation between the designed travel times from the GTFS Schedule data and the actual travel times imputed from the GTFS-RT feeds. The research demonstrates an innovative approach to transform probe data generated by GTFS-RT feeds into valuable measures of transit performance at segment-level granularity. The research emphasizes the importance of segment-level transit performance measurement while recognizing that the methods used to impute segment time points are likely to change over time as the GTFS-RT standard evolves.]]></description>
      <pubDate>Wed, 27 Nov 2024 13:41:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2449511</guid>
    </item>
    <item>
      <title>ChatGPT for GTFS: benchmarking LLMs on GTFS semantics... and retrieval</title>
      <link>https://trid.trb.org/View/2417452</link>
      <description><![CDATA[The General Transit Feed Specification (GTFS) standard for publishing transit data is ubiquitous. With the advent of LLMs being used widely, this research explores the possibility of extracting transit information from GTFS through natural language instructions. To evaluate the capabilities and limitations of LLMs, we introduce two benchmarks, namely “GTFS Semantics” and “GTFS Retrieval” that test how well LLMs can “understand” GTFS standards and retrieve relevant transit information. We benchmark OpenAI’s GPT-3.5 Turbo and GPT-4 LLMs, which are backends for the ChatGPT interface. In particular, we use zero-shot, one-shot, chain of thought, and program synthesis techniques with prompt engineering. For our multiple questions, GPT-3.5 Turbo answers 59.7% correctly and GPT-4 answers 73.3% correctly, but they do worse when one of the multiple choice options is replaced by “None of these”. Furthermore, we evaluate how well the LLMs can extract information from a filtered GTFS feed containing four bus routes from the Chicago Transit Authority. Program synthesis techniques outperformed zero-shot approaches, achieving up to 93% (90%) accuracy for simple queries and 61% (41%) for complex ones using GPT-4 (GPT-3.5 Turbo).]]></description>
      <pubDate>Mon, 16 Sep 2024 08:56:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2417452</guid>
    </item>
  </channel>
</rss>