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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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Evolution of Mode Choice: Examining the Relationship Between Telecommuting and Transit Use</title>
      <link>https://rip.trb.org/View/2702855</link>
      <description><![CDATA[This project aims to quantify the impacts of telecommuting on transit use. Data for this analysis is derived from the 2019 and 2023 editions of the Puget Sound Regional Council (PSRC) household travel survey and a joint model of telecommuting and transit use frequency is estimated to understand the nature of the relationship in the pre- and post-pandemic periods. The findings reveal a U-shaped relationship between telecommuting and transit use. Lower transit frequency was observed at both the highest and lowest levels of telecommuting, while higher transit frequency was associated with medium or hybrid levels of telecommuting. This pattern became even more pronounced in 2023. Computations of average treatment effects show that transitioning from medium-level (hybrid) telecommuting to non-telecommuting resulted in a 21 percent decrease in transit use in 2019, and a steeper 35 percent decrease in 2023. Similarly, moving from hybrid to frequent telecommuting led to a six percent reduction in transit use in 2019, and a larger nine percent reduction in 2023. These findings suggest that the loss in transit ridership in the post-pandemic era is likely to persist and that compelling workers to return to the workplace full-time is unlikely to yield significant gains unless transit agencies find innovative ways to attract non-telecommuters (full commuters) back to transit. Instead, embracing a hybrid work modality while providing incentives to promote transit use may yield greater benefits.]]></description>
      <pubDate>Thu, 14 May 2026 15:50:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702855</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>Transit-Oriented Development Ridership Calculator - Phase 1</title>
      <link>https://rip.trb.org/View/2697837</link>
      <description><![CDATA[Transit-Oriented Development (TOD) is the creation of compact, walkable, pedestrian-oriented, mixed-use communities centered around stations along transit priority corridors (rail, bus rapid transit). The State of California has established several policies to support TOD, as have local jurisdictions and the Federal Transit Administration (FTA). There is a need for research on the quantifiable impacts of TOD on transit ridership to guide policy and investments in TOD for future transit plans. There is good literature on the impacts of various factors (fares, gas prices, service levels, TNCs, etc.) on ridership levels. There is also literature on the impact of land use generally (employment/population density, land use types) on transit ridership. TOD impacts on ridership, however, are not well researched to date because data on TODs and the residents that move into them is not available at a consistent level. Agencies and municipalities applying for state grants provide estimates of ridership impacts and new development catalyzed by the project, but producing well-supported, reliable numbers in their applications remains a challenge. Some regions have regional ridership models they use in service planning, but these models are generally not designed to forecast changes associated with a single building or neighborhood. Other regions do not have ridership models at all and must rely on ad-hoc approaches to estimate ridership impacts. There is also a need to provide consistent, transparent transit ridership estimates so that more-resourced agencies applying for grants do not out-compete less-resourced agencies that don't have their own regional ridership models. Different local or regional transit ridership model methodologies may not be transparent to audiences, including grant application evaluators. One way to standardize the process and level the playing field between agencies is through the creation of an easy-to-use calculator for estimating the ridership impacts of TOD projects. Through this tool, ridership estimates for applicants seeking funding or zoning variances for TOD projects will be able to document how the projects will impact ridership. These estimates will be comparable across projects thanks to using a standardized methodology. This project will lay the groundwork for creating such a calculator, by reviewing the existing literature on the topic and identifying an actionable plan and concrete model structure to implement the calculator.]]></description>
      <pubDate>Thu, 30 Apr 2026 12:20:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697837</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>Synthesizing Microtransit and Fixed Route Transit via Rider Hand Off to Improve Transit Efficiency</title>
      <link>https://rip.trb.org/View/2640190</link>
      <description><![CDATA[Microtransit programs can improve local mobility, but they often operate separately from fixed route bus networks. This separation can create gaps in connectivity and reduce the potential efficiency of both systems. This project will study how rider hand off strategies, where microtransit vehicles bring passengers directly to fixed route transit, can strengthen system performance. Using data from CTtransit, microtransit logs, and synthetic demand models, the research will simulate multimodal operations and evaluate how pickup schedules and transfer points influence wait times, travel times, and network utilization.

The project will develop an optimization framework to identify operating strategies that improve rider transfers and increase the efficiency of both modes. Scenario testing will measure the effects of integration on cost, ridership patterns, and service quality. The results will provide agencies with practical guidance on how to coordinate microtransit and fixed route services in ways that improve reliability and expand access to transit. These findings can support broader efforts to enhance mobility in Connecticut and inform similar initiatives in other regions.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:47:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640190</guid>
    </item>
    <item>
      <title>Quick-Response Research on Long-Term Strategic Issues. Task 56. Student Fare Programs to Increase Ridership</title>
      <link>https://rip.trb.org/View/2636148</link>
      <description><![CDATA[Public transportation agencies across the U.S. have implemented student fare and pass programs in order to broaden their ridership base and provide new agency revenue streams. There exist a wide variety of programs and fare offerings for both university and secondary students, with different goals and funding sources.  Research suggests that exposure to high-quality transit in one’s 20s and 30s increases the likelihood of using transit later in life. Encouraging transit use by students can have positive long-term ridership impacts for the entire transit industry. 
TCRP Synthesis 131 examined university pass programs in a limited way in 2018. Since then, many more agencies have adopted these programs. Student fare programs vary widely depending on transit market, regional operating structure, fare systems, and types of institutions served.
Public transit agencies seeking to adopt new student fare programs or expand an existing program face uncertainty regarding costs, program feasibility, impacts on ridership and operations, and overall benefits. Research into the wide variety of student fare programs would assist in these evaluations.
The objective of this research is to provide information to assist transit agencies and policymakers in adopting and expanding student fare programs. The study should: Examine student fare programs at agencies and in regions of varying sizes, serving a range of student populations (secondary, community college, university, etc.); Examine funding mechanisms for student fare programs; Identify successful partnership models between transit agencies, and between agencies, schools and higher education; Evaluate program successes in terms of ridership impacts and travel behavior changes; Evaluate the operational impacts and challenges of student fare programs; Investigate transit operator/other worker perspectives; Assess opportunities and challenges for scaling student fare programs in different types of communities (urban, suburban, rural).]]></description>
      <pubDate>Mon, 08 Dec 2025 20:08:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2636148</guid>
    </item>
    <item>
      <title>Innovations Deserving Exploratory Analysis--The Transit IDEA Program. Transit Ridership Prediction (TRiP) Tool</title>
      <link>https://rip.trb.org/View/2569129</link>
      <description><![CDATA[Transit agencies need up-to-date ridership forecasts to estimate fare revenue and make regular service changes. However, ridership forecasting is not easy, particularly for mid-size and small-size transit agencies that do not have the resources to develop complex models, considering the many factors that affect ridership levels. 

To help  transit agencies in this need, this project will develop an open-source forecasting tool for estimating ridership. The forecasting model would use regression techniques and will include variables such as service levels (e.g., vehicle revenue miles), population, employment, gas prices, and telecommuting. Data needed to create the forecasting model will be compiled and will include historical route-level ridership and service provision data that transit agencies regularly collect. Other publicly available data from the US Census Bureau (e.g., population), the US Bureau of Labor Statistics (e.g., employment/unemployment levels), and the Energy Information Administration (e.g., gas prices) will also be obtained. Using this data, a model will be developed as a script using software such as the open-source statistical program R. The model outputs will be ridership response (elasticities) to changes in different internal and external factors, which would be used for ridership forecasting. The forecasting model will be web-based and can be used by any bus-based transit agency to create forecasts specific to that agency's region. The Nashville Public Transit system and the University of California Transit system at Davis will implement the developed tool. A user guide and training videos will be prepared that will describe in more detail how to use and customize the tool.

The proposed open-source tool will offer several benefits to the transit agencies. First, the model will forecast ridership at the route level, which will provide agencies flexibility to forecast ridership based on changes in specific areas in the city that might affect only one or a few routes. Second, these forecasts can be updated annually with minimal effort/time and with little to no cost. Lastly, the tool would be customizable, which will allow agencies to use additional variables (e.g., reliability) based on their local data availability. ]]></description>
      <pubDate>Mon, 23 Jun 2025 20:41:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2569129</guid>
    </item>
    <item>
      <title>An Evaluation of the Long-Term Effects of the COVID-19 Pandemic on Public Transportation Use</title>
      <link>https://rip.trb.org/View/2553165</link>
      <description><![CDATA[Public transportation provides many advantages compared to other transportation modes. It offers a cost-effective commuting opportunity, plays an important role in reducing traffic congestion and carbon emissions, and promotes equity among travelers. However, recent changes in transportation behavior, largely influenced by the COVID-19 pandemic, have resulted in a decline in transit ridership, posing challenges for the future of this mode. While there is evidence of significant rebounds in ridership from pandemic lows, transit has not fully recovered. Continued fears of safety, service cuts, new travel habits, evolving work arrangements, and the growth of online activity participation have all contributed to the slow recovery of public transportation. In this context, an in-depth and rigorous study is needed to assess the ongoing and long-term effects of the pandemic on public transportation use. To explore these changing dynamics, the research team examines the changes in individual-level use of public transportation since the onset of the pandemic, as well as the possible return to pre-pandemic behaviors. Using data from the 2022 National Household Travel Survey, the team considers the self-reported impact of the pandemic on public transportation ridership and the expected permanence of this impact. The proposed investigation will allow the team to identify individuals who have altered their public transportation use since the pandemic, and distinguish which groups of individuals may be willing to return to previous ridership levels. In addition, the research will also examine the characteristics of current users of public transportation. Taken together, the results will have important implications for ongoing and future public transportation policies, providing insights into future mobility trends and informing strategies to improve public transportation ridership. ]]></description>
      <pubDate>Thu, 15 May 2025 14:50:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553165</guid>
    </item>
    <item>
      <title>Improving Transit Agency (Commuter Rail) Performance to Attract Riders: What Works?</title>
      <link>https://rip.trb.org/View/2518948</link>
      <description><![CDATA[The purpose of this project is to identify successful strategies that commuter rail providers have used to rebuild their ridership to counter stagnant or declining ridership in the post-COVID–19 era. It will also identify how transferable these strategies are to other areas by assessing whether they involved local partnership and/or were based on local conditions.]]></description>
      <pubDate>Mon, 03 Mar 2025 19:12:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2518948</guid>
    </item>
    <item>
      <title>Developing a Transportation Demand Management Tool for DRPT to Understand the Factors Affecting Transit Ridership</title>
      <link>https://rip.trb.org/View/2499025</link>
      <description><![CDATA[This research aims to develop a Transportation Demand Management (TDM) evaluation tool for the Virginia Department of Rail and Public Transportation (DRPT). TDM strategies aim to reduce dependence on single-occupant vehicles and promote more sustainable modes such as public transportation. One traditional way to understand the impact of a single TDM strategy on mode choice would be to conduct before and after surveys. Without implementing the TDM strategy, forecasting the effect of that specific TDM strategy on transit ridership is not possible. To address this issue, this research will develop a tool to assess the effect of a single TDM strategy or a combination of multiple TDM strategies on individuals' mode choices. A joint revealed preference (RP) and stated preference (SP) survey will help understand individuals' preferences towards certain TDM strategies in Virginia. An advanced joint RP-SP model will be estimated based on the collected data. This model will be integrated into a flexible tool, so staff from Virginia Department of Transportation (VDOT), DRPT, transit agencies, and TDM organizations could use the tool to estimate the effect of various TDM policies on transit ridership and mode choice behavior. ]]></description>
      <pubDate>Tue, 28 Jan 2025 11:44:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2499025</guid>
    </item>
    <item>
      <title>Understanding travel pattern changes of transit-reliant population from before to during and after heat wave: a case study for Seattle</title>
      <link>https://rip.trb.org/View/2458997</link>
      <description><![CDATA[This project will use ORCA data from King County Metro (KCM), the main transit company for the Seattle metropolitan area, to understand how travel patterns for the transit-reliant population changed during the 2023 heat wave in Seattle. The project will answer the following: How did travel patterns change from before, to during and after the heat wave for Seattle’s transit reliant population? Who were able to adapt during a heatwave? What was the level of accessibility to cooling spaces (green spaces, cooling centers offered by the government) during the heat wave for transit-reliant population? How changes in ridership during heatwave correlate with changes in traffic?]]></description>
      <pubDate>Thu, 21 Nov 2024 16:15:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458997</guid>
    </item>
    <item>
      <title>Envisioning Micromobility as Public Transit: Two intervention studies in the living lab of Davis, California</title>
      <link>https://rip.trb.org/View/2431628</link>
      <description><![CDATA[Mode share in public transit in the United States traditionally lags behind other developed countries. One promising strategy to encourage public transit use involves enhancing access and egress from transit stops. Given the potential of shared micromobility services to address the "last mile" challenge, there is growing interest in integrating these services into public transit services. However, achieving affordability and access in micromobility services poses challenges for operators in ensuring sustainable operations at appropriate pricing levels. The cost of using micromobility services has sharply increased in recent years, making it unaffordable for many people. Concurrently, the researchers’ recent research suggests there is consensus across private industry, government, and advocates that micromobility can best serve the public if it is viewed as a public transit option. To begin to envision micromobility as serving existing public transit and acting as public transportation itself, the researchers will examine the role of pricing on micromobility demand. In this project, the researchers will conduct two pricing-focused field experiments, partnered with the micromobility operator, SPIN, and a railway operator, Capitol Corridor. The first experiment will use the railway station of the Capitol Corridor in Davis, California as a living lab to assess the effectiveness of increasing rail usage by subsidizing micromobility services. The second experiment will focus on micromobility services operated by SPIN in Davis, aiming to understand the general price elasticity of demand for micromobility. Through these experiments, the researchers will analyze the causal effects of the interventions on increasing railway and micromobility use. The insights gained from this analysis will provide valuable guidance on the potential of micromobility and regional rail partnerships to enhance transit use in other corridors throughout the state as well as pricing mechanisms to understand the potential for micromobility services to satisfy the travel demand of communities. ]]></description>
      <pubDate>Wed, 18 Sep 2024 19:12:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431628</guid>
    </item>
    <item>
      <title>Mobility and Funding Assessment for the Expansion of Free and Reduced Fares</title>
      <link>https://rip.trb.org/View/2417473</link>
      <description><![CDATA[The aim of this project is to conduct an assessment for the expansion of free and reduced fares in Illinois’ public transit systems to increase transit ridership. Researchers will investigate the impact of existing free and reduced fare programs, assess potential expansion strategies and provide recommendations for improved equity and service efficiency. Effectively developing programs for the expansion of free and reduced transit fares will allow transit agencies to enhance their revenue streams as well as provide affordable fares.]]></description>
      <pubDate>Fri, 16 Aug 2024 09:23:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2417473</guid>
    </item>
    <item>
      <title>The Efficiency and Affordability of Automated Vehicle Passenger Services in Rural and Under-Served Communities</title>
      <link>https://rip.trb.org/View/2384856</link>
      <description><![CDATA[We propose to assess the expected efficiency and affordability of automated vehicle (AV) passenger services, such as Waymo and Cruise, in rural and underserved communities. Our methodology involves applying an innovative model to predict the human-driven ride-hailing demand (Uber and Lyft) for every Census tract in the Southeast, assessing the potential for AVs to reduce the cost of providing equivalent service, and predicting how the demand would change in response to cost reductions.  We have already developed the predictive model, and via this CR2C2 REEG grant, we will quickly scale it up for large scale application and test its spatial transferability against observed data in rural areas. The results will enable transportation policy makers to engage with the AV companies in an informed way as the companies continue their roll-out in the Southeast.]]></description>
      <pubDate>Thu, 30 May 2024 19:01:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2384856</guid>
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