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    <title>Research in Progress (RIP)</title>
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    <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>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Physics-Informed AI-Enhanced Multimodal Modeling and Governance: Improving Safety and Resilience for Data-Limited Transit Corridors</title>
      <link>https://rip.trb.org/View/2739297</link>
      <description><![CDATA[Limited sensor coverage and fragmented, mode-specific modeling infrastructure hinder the holistic monitoring of modern transportation networks. The resulting data blind spots prevent current models from capturing dynamic, cross-modal dependencies, where a disruption in one mode, such as a metro closure, triggers cascading surges in others, forcing planners and Traffic Management Centers to rely on reactive, siloed strategies.

To improve the state of the art, this project proposes a Virtual Sensor paradigm driven by physics-informed generative artificial intelligence (AI). By integrating fundamental transportation physics with generative deep learning, the framework synthesizes high-fidelity data for sensor-sparse regions by inferring correlations from existing sensing infrastructure, creating cost-effective virtual data streams that simulate physical sensors and provide more complete multimodal network data for real-time operations and long-term planning. The project also evaluates the policy and governance dimensions of integrating emerging AI use cases, such as AI-generated data, into the Delaware Department of Transportation (DelDOT)’s planning, design, and operations.]]></description>
      <pubDate>Thu, 30 Jul 2026 16:41:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2739297</guid>
    </item>
    <item>
      <title>Deriving Transit Performance Metrics from GTFS Data</title>
      <link>https://rip.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://rip.trb.org/View/2732356</guid>
    </item>
    <item>
      <title>Evaluation of Transit Signal Display Options</title>
      <link>https://rip.trb.org/View/2716608</link>
      <description><![CDATA[There is a need to assess the operational and safety impacts of alternative transit signal
displays and inform recommendations to be incorporated in state and national transit signal
design guidelines. OBJECTIVES: 1. Understand the current state of practice on the implementation of transit signal displays through a thorough review of the literature and outreach to transit agencies and other relevant stakeholders. 2. Explore transit operator preferences regarding positioning and display of transit signals through a survey to transit operators. 3. Investigate correlations between crashes and transit signal displays, through crash report analyses. 4. Understand driver behavior when encountering transit signals through field observations, static evaluation surveys, and driving simulation. 5. Develop recommendations for consideration in the next version of the Manual on Uniform Traffic Control Devices (MUTCD) regarding positioning and display design of transit signals.]]></description>
      <pubDate>Thu, 18 Jun 2026 10:03:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2716608</guid>
    </item>
    <item>
      <title>Assessing the Reliability and Usability of Mobile Ticketing App Data for Transit Analytics: A Case Study of Unitrans in Davis, California</title>
      <link>https://rip.trb.org/View/2702581</link>
      <description><![CDATA[Mobile ticketing apps have become increasingly popular among transit agencies due to their cost efficiency and ability to streamline payments. Beyond operational efficiencies, these apps also generate vast travel data with the potential to support transit agencies in decision-making. However, this data contains incomplete trip information and suffers from representation bias. Several questions remain unanswered: Is this data representative of all transit riders? If so, what are the potential applications? 

This project will address this gap by evaluating the potential applications and representativeness of app data. The research will focus on ZipPass, a mobile ticketing app used by Unitrans in Davis, California. To date, ZipPass has already generated over one million spatial activation records. The project team devised a strategy to integrate ZipPass data with the onboard transit survey and the UC Davis campus travel survey. The team will also conduct a targeted survey of active ZipPass users to supplement rider-specific and trip-level information. The project will explore how ZipPass data, along with support from supplementary data sources, can be used for two potential applications to support the agency: (1) estimating transit ridership and (2) understanding riders' origin-destinations. 

The research will provide valuable insights to transit agencies looking to harness mobile ticketing data for operational purposes. Since periodic onboard transit surveys are required for federal funding, both mobile ticketing data and transit survey data are available to agencies at no extra expense. Small agencies can leverage our findings to integrate at least these two datasets and effectively utilize them for operational improvement.]]></description>
      <pubDate>Thu, 14 May 2026 16:51:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702581</guid>
    </item>
    <item>
      <title>Evaluating Behavioral Responses to Mobility Credits and Ridehailing Integration in a Digital Mobility System</title>
      <link>https://rip.trb.org/View/2702725</link>
      <description><![CDATA[Digital mobility platforms are increasingly adopted by public agencies to coordinate multimodal travel, streamline fare payment, and improve efficiency. However, there is limited empirical evidence on how users respond to platform-based incentives and integrated services in real-world settings, as most studies rely on stated preference data or simulations. This project analyzes user behavior on Vamos-EZHub, a public digital mobility platform that integrates trip planning, fare payment, and access to services including local transit and ridehailing. It evaluates behavioral responses to two sequential interventions on Vamos-EZHub: (1) the introduction of prepaid mobility credits and (2) the integration of a transit-triggered ridehailing credit. 

Using longitudinal platform telemetry, ridehailing trip records, transit fare activation data, and General Transit Feed Specification (GTFS) data, the project examines how mobility and ridehailing credits affect platform engagement, transit and ridehailing use, first/last-mile connectivity, and spatial and temporal patterns of linked travel. Two-way fixed effects and event-study models are used to identify behavioral changes associated with each intervention. A geospatial-temporal algorithm classifies ridehailing trips connecting to transit, and stop- level regression models identify transit service and network characteristics associated with demand for linked trips. 

Expected outcomes include quantitative estimates of the influence of mobility credits and ridehailing integration on multimodal coordination, identification of service characteristics associated with higher demand for linked trips, and a reproducible analytical framework. The results will inform data-driven platform design, operational planning, and integration strategies for public agencies managing digital mobility platforms, while providing evidence to guide coordination with private ridehailing partners to improve system efficiency and reliability.]]></description>
      <pubDate>Thu, 14 May 2026 16:36:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702725</guid>
    </item>
    <item>
      <title>Putting a Price on Regional Rail Quality: Evaluating the Value Potential Riders Place on Regional Rail Service Attributes</title>
      <link>https://rip.trb.org/View/2702083</link>
      <description><![CDATA[Public transit has suffered from chronic disinvestment despite its community-wide benefits. Post-pandemic, drastic changes in travel demand have left agencies grappling with financial stress. California’s transit ridership has generally tracked alongside national ridership trends with a substantial dip in ridership and then slow recovery, but commuter rail mode share has remained substantially lower than pre-pandemic shares. Most rail services are geared towards serving commuters; higher frequency is offered during weekdays and peak hours, ticket pricing is tailored to favor people making the same kind of trip on a regular basis, and service hours align with commuter needs. The five days-a-week commuting to work lifestyle is no more, and rail agencies serving commuters are experiencing decimated ridership that is showing no signs of bouncing back. This project uses survey research targeted towards understanding how to tailor rail services to gain new markets for regional rail services. The research team developed a stated preference (SP) experiment to understand evolving needs of commuters and non-commuters, as well as riders and potential riders. The service attributes under study include train schedule, ticket cost, station access, reliability, station amenities, and how the potential user base views rail services. Although the study will focus on the area defined by its research partner, Capitol Corridor, it is widely applicable across the country in locations with intercity, suburban, and small urban regional rail services.]]></description>
      <pubDate>Wed, 13 May 2026 16:58:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702083</guid>
    </item>
    <item>
      <title>Zero- and Reduced-Fare Transit Policy and Post-Pandemic Recovery: A Multi-Agency Analysis of Ridership, Service Supply, and Access</title>
      <link>https://rip.trb.org/View/2697838</link>
      <description><![CDATA[Despite growing interest in fare reduction as a policy lever, rigorous comparative evidence on its effects, particularly in the post-pandemic context, remains limited. In Virginia some 40 transit agencies eliminated fares for some period of time during the COVID pandemic, leading the Department of Rail and Public Transportation to ask how fare reduction or fare elimination have affected ridership, operations, and access for system users. This research addresses that question by developing a structured analytical framework and applying it to a sample of transit agencies in which Virginia properties are heavily represented. Using longitudinal data spanning years before and after the pandemic “lockdown”, the research compares agencies that adopted zero- and reduced-fare policies or means-tested fare-free programs against matched fare-collecting agencies. The analysis addresses three interrelated outcomes: ridership recovery trajectories, changes in service supply and scheduled speeds and headways, and shifts in access to employment and key destinations. For a representative subset of agencies, the study also conducts a network-level analysis of access to employment using Remix, a transit planning and scheduling software tool, for a selected set of agencies that represent a range of system sizes. Findings are intended to provide evidence-based guidance for Virginia transit agencies and other stakeholders considering fare policy as a tool for ridership recovery and service quality and performance.]]></description>
      <pubDate>Thu, 30 Apr 2026 08:37:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697838</guid>
    </item>
    <item>
      <title>Accounting for fare evasion in estimates of transit ridership</title>
      <link>https://rip.trb.org/View/2697836</link>
      <description><![CDATA[Fare evasion has been a problem for transit agencies since the days of horse-drawn omnibuses. However, over the past few years fare evasion rates have been up across the country. In New York City, the Metropolitan Transit Authority estimated that 48% of bus riders did not pay, compared to 18% pre-COVID. Similar trends have been observed in other parts of the country, including in California. Fare evasion can have many impacts for the transit agency and its riders. First and foremost, it results in a loss of revenue at a time when agencies are fighting to maintain operations funding at acceptable levels. For this reason, agencies regularly attempt to combat fare evasion and have many techniques to do so both in practice and from the literature. However, fare evasion may also impact the ability of an agency to estimate their ridership. It is vital to understand ridership trends in general and specifically ridership recovery. If increasing fare evasion rates are not accounted for, the data used to gauge transit’s recovery in terms of ridership may be systematically wrong. While transit agencies must report their transit ridership data to the National Transit Database in the form of Unlinked Passenger Trips (UPT) and Passenger Miles Traveled (PMT), no information is systematically available about how the data are collected. The main objective of this research is to assess and document methods used to determine UPT and PMT at transit agencies across California, including sources of potential error such as fare evasion. The predominant source of this data will be a statewide transit agency survey. To reduce respondent burden, the second objective of this research is to develop a new initiative across the state to coordinate research involving surveys or other large outreach efforts across multiple transit agencies in partnership with Caltrans, state agencies, advocacy organizations, and research organizations. It supports a key area of responsibility with regard to tracking and reporting transit performance measures and assessment of the suitability of new transit investments.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:26:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697836</guid>
    </item>
    <item>
      <title>How Do People Receive Information About Public Transit?</title>
      <link>https://rip.trb.org/View/2696850</link>
      <description><![CDATA[Providing transit information helps passengers adapt when service is unreliable and has been shown to decrease wait times, reduce overall travel time, increase ridership, increase satisfaction with transit, and increase perceptions of personal security. However, to date, there is limited evidence for how riders prefer to access and use transit information. A variety of methods are available, including websites, apps, signage, and transit ambassadors or drivers, but which methods of information are most used by riders and how does it differ by the type of rider and type of trip? Riders need real time information to be accurate, but how does inaccurate information impact their trip? In addition, how do riders plan their travel pre-trip, such as understanding hours and frequency of service, finding the stop, and understanding payment mechanism? This research aims to explore how both transit riders and non-riders access public transit information for the purpose of planning and taking trips on transit to answer these questions. This work will improve understanding of customer perspective to aid agencies in providing better transit rider information in a cost-effective manner, thus improving the long-term viability of the transportation system by increasing demand for transit.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:12:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696850</guid>
    </item>
    <item>
      <title>Car or Public Transit? Exploring Factors Affecting Mode Choice in the Mobility of Care</title>
      <link>https://rip.trb.org/View/2696849</link>
      <description><![CDATA[This project explores the factors affecting mode choice decisions in the mobility of care. By applying the discrete choice modeling of Nested Logit (NL) and the Multiple Discrete Continuous Extreme Value (MDCEV) to the 2022 National Household Travel Survey (2022 NHTS), this study explores factors regarding where transit service may better accommodate travel needs in serving care trips. Also, it highlights how public transit contributes to reducing household travel generated by care trips. The researchers expect to obtain distinct characteristics of care trips and the socio-demographics of those who travel for care to inform the California Department of Transportation (Caltrans) which transit-related manuals, guidelines, and policies are critical to accommodate travel and their mobility of care. Results are also useful for transit agencies when considering aspects to improve their service in care trips.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:10:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696849</guid>
    </item>
    <item>
      <title>Continuous approximation models for rural transit network design</title>
      <link>https://rip.trb.org/View/2691671</link>
      <description><![CDATA[The purpose of this project is to discover new continuous approximation models for public transit network design, with a specific focus on rural areas where access to transit and coverage present significant challenges. Rural transit systems face unique constraints in connecting dispersed population centers while maintaining economic viability, which necessitates a modelling approach that addresses multiple competing objectives simultaneously. The continuous approximation paradigm is a quantitative method for solving logistics problems using a small set of parameters to model a complex system, which results in simple algebraic expressions that are easier to manage than (for example) large‐scale optimization models. As a further benefit, one often obtains insights from these simpler formulations that determine what affects the outcome most significantly. Although continuous approximation models have been used for over 60 years in logistics systems analysis, there has been very little research conducted on their application to problems in rural transit networks, likely due to their distinctive spatial characteristics and coverage requirements. Recent research demonstrates that limited flexibility yields disproportionate benefits in logistics systems. This project will combine tools from geospatial optimization, computational geometry, and geometric probability theory to formulate new models that will solve these problems. Furthermore, these models will identify which complementary infrastructure investments would most effectively increase transit availability and ridership in rural counties. The research outcomes include both theoretical advances in continuous approximation methodology and practical planning tools for rural transit agencies with limited computational resources.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:45:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691671</guid>
    </item>
    <item>
      <title>Micromobility Decision-Making Atlas</title>
      <link>https://rip.trb.org/View/2669653</link>
      <description><![CDATA[This work will examine how U.S. micromobility users make everyday travel and safety decisions. Participants will be identified from two experience groups: riders who integrate e-scooters or e-bikes with public transit and those who substitute them for car trips. Situated within the broader mixed-methods design, this project builds directly on the “Healthy Micromobility: Moving from Crisis to Opportunity” pilot project. It will provide explanatory depth on the psychosocial and contextual mechanisms that shape micromobility use and user safety. These findings will also inform the system-level analyses by clarifying how user experiences and perceptions translate into behavioral, safety, operations, and other relevant outcomes.   

A micromobility decision-making atlas will be designed to serve as a current, comprehensive database of local micromobility regulations and policy environments across U.S. jurisdictions, providing an updated and more detailed successor to existing resources such as the Shared-Use Mobility Center’s Policy Atlas. The atlas would compile and standardize policy data from the environmental scans, allowing users to explore and compare domains such as fleet management, parking, speed limits, and accessibility provisions. An optional infrastructure layer could incorporate indicators of supportive design conditions, such as protected lane coverage or PeopleForBikes Bicycle Network Analysis scores, to contextualize how local infrastructure aligns with policy intent.  ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:30:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669653</guid>
    </item>
    <item>
      <title>Improving Public Transportation in Rural Areas and Tribal Communities</title>
      <link>https://rip.trb.org/View/2663577</link>
      <description><![CDATA[NCHRP Research Report 1144/TCRP Research Report 251: Public Transportation in Rural Areas and Tribal Communities: Guide for Service Improvements provides a guide to help public transit agencies in rural and tribal communities initiate new or enhance existing public transportation services to improve mobility and accessibility, enhance performance, and consider appropriate innovations. The guide will help transit providers determine the service designs and modes that are most appropriate within their community and support implementation of those services. This guide will be of immediate use to public transportation providers and stakeholders that plan and provide public transportation services in rural areas and tribal communities throughout the United States.    ]]></description>
      <pubDate>Mon, 02 Feb 2026 19:27:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663577</guid>
    </item>
    <item>
      <title>Using Artificial Intelligence to Uncover How Safety Perception Influences Travel Behavior Shifts: Comparative &amp; Longitudinal Analysis for the Future of Autonomous Vehicle, Transit and Ride-hailing Services</title>
      <link>https://rip.trb.org/View/2655700</link>
      <description><![CDATA[Transit agencies and cities are increasingly overwhelmed by large volumes of unstructured data; yet they lack methodical, validated tools to turn safety narratives into operational indicators. This project addresses that gap by measuring and comparing public safety perception for autonomous-vehicle services (robotaxis), public transit, and ride-hailing services. It will assess how these perceptions relate to traveler profiles and mode choice in San Francisco and San Jose over a six-month period. San Francisco as a mature setting where robotaxis may compete with ride-hailing and transit, and San Jose as a newer coming deployment that provides a baseline for comparison and forward-looking extrapolation.
The research team will use artificial intelligence with human-audited classification to analyze public discourse drawn from news-comment threads and social-media posts, for example, discussions of disengagements, curb conflicts, yielding behavior, and interpersonal harm such as unwanted contact, theft, or assault. Validation will include human audit with inter-rater reliability (aiming for Cohen’s kappa of at least 0.60), time- and city-based cross-validation, and an error taxonomy with documented adjustments. The project will deliver (1) a transparent safety-perception taxonomy, (2) traveler-persona profiles linked to safety perceptions, (3) a lightweight dashboard for agencies and cities to explore time, place, and topic trends, and (4) operational and policy frameworks for improvements across all modes, organized into vehicle-level safety measures, station and hub operating practices, reporting and response mechanisms, and rider communication standards. The approach and workflow are replicable and can be extended to additional cities. The innovation lies in a reusable tool bridging research and practice providing concrete, methodical steps to turn qualitative narratives into consistent indicators they can trust. Agencies can adopt it to sort and prioritize incoming signals, rerun it with new data, and compare results across time and places to support day-to-day decisions and longer-term planning.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:09:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655700</guid>
    </item>
    <item>
      <title>Improving Public Transportation in Rural Areas and Tribal Communities</title>
      <link>https://rip.trb.org/View/2652143</link>
      <description><![CDATA[Rural and tribal communities have a wide range of public transportation needs and must tailor their transit services, operations, funding strategies, and organization to the conditions within their communities. These transit agencies have different programs and practices for overseeing, funding, and managing their services. Insufficient financial and staffing resources make it challenging for transit providers in rural areas and tribal communities to identify and fully meet the travel needs of the communities they serve, to communicate effectively with patrons, to comply with national requirements, and to evaluate and adopt new technologies.

Funded jointly under NCHRP Project 08-147/TCRP Project B-49,  “Improving Public Transportation in Rural Areas and Tribal Communities,” KFH Group was asked to produce a guide to (1) promote practices that are responsive to customers and aid transit providers in improving efficiency and effectiveness; (2) help transit providers better leverage and coordinate resources, comply with federal requirements, and adopt appropriate emerging technologies; and (3) address the differences  among rural areas and tribal communities regarding distinguishing characteristics and how these characteristics affect public transportation services. The distinguishing characteristics include size (i.e., square miles), population density, demographics, current mobility options, economic conditions, proximity to small, medium, and large urban areas, proximity to health facilities, geography, road infrastructure, internet connectivity, funding, and weather conditions.

The guide has four parts:  Part I presents new and emerging strategies and practices to enhance rural and tribal transportation services. Part II focuses on topics that support or improve the strategies and practices, presented in eight chapters: planning for new service strategies; management and operations considerations; communications with current and prospective passengers; safety and security; technology; measuring success; funding and revenue; and accessible service. Part III addresses topics that will help rural and tribal transit agencies improve or enhance their transportation services.  Finally, Part IV introduces nine case studies that detail services or strategies implemented to improve public transportation for the rural or tribal communities.  

Supplemental to the guide are the full nine case studies and the appendix which presents the literature review.  The supplemental documents are available on the National Academies Press website (nap.nationalacademies.org) by searching for NCHRP Research Report 1144/TCRP Research Report 251: Public Transportation in Rural Areas and Tribal Communities: Guide for Service Improvements and looking under “Resources.”]]></description>
      <pubDate>Thu, 15 Jan 2026 14:12:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652143</guid>
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