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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>Strategic Interactions in Mixed-Autonomy Networks: Multi-Company AV/HV Fleet Operations and Policy Implications</title>
      <link>https://rip.trb.org/View/2786713</link>
      <description><![CDATA[Autonomous vehicle (AV) ride-hailing fleets are no longer hypothetical: Waymo, Uber, and competing operators are deploying mixed AV and human-driven fleets across U.S. cities. State and local agencies must now decide which operating rules to impose, such as permit caps, congestion pricing zones, and limits on empty repositioning trips, without analytical tools that capture how multiple competing fleet operators actually respond to those rules and to one another. Existing models treat AV fleets as if they were human-driven fleets with different cost structures, missing the central control, repositioning capability, and regulatory susceptibility that distinguish AV operations. This project develops a two-level modeling framework for operational constraints and regulatory design in mixed AV and human-driven ride-hailing networks. The lower level represents the strategic interaction among competing fleet operators, travelers, and the road network, extending the team's prior network equilibrium model to include empty-vehicle repositioning that loads network links and human drivers who work for multiple platforms simultaneously. The upper level represents a regulator selecting permit caps, congestion-pricing tolls, and repositioning limits to optimize a system-level objective such as total travel time or system congestion. The team will develop solution methods, release an open-source solver, and apply the framework to representative California networks. The work will give state and local agencies a transparent, defensible tool to evaluate regulatory levers before deployment. ]]></description>
      <pubDate>Mon, 05 Oct 2026 13:38:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786713</guid>
    </item>
    <item>
      <title>Economic Contribution of the North Carolina Ferry System</title>
      <link>https://rip.trb.org/View/2781131</link>
      <description><![CDATA[The North Carolina Ferry Division operates vessels on routes along the eastern coast of North Carolina. The routes serve diverse populations, ranging from routes with substantial tourist/visitor customers to routes with primarily daily commuters. The Ferry Division is also responsible for operating a fleet of support vessels which perform marine maintenance and dredging activities. Additionally, the Ferry Division operates a shipyard for vessel repairs, located in Manns Harbor. A 2020 study (NCDOT RP 2018-11: Economic Contribution of the North Carolina Ferry System) estimated the economic contribution of the Ferry Division operations. An updated study is needed to fully describe the contribution of the Ferry Division on the local, regional, and state economy since the time of the previous study.
The project will seek to understand customer demand, customer characteristics, and the resulting economic contribution relating to usage of the NC ferry system. Additionally, this project will investigate the economic contribution of the operation of the ferry vessels, dredging activities, and the shipyard. Three essential data sets will be used to derive the economic contribution values: (1) surveys of passengers (conducted in this study), (2) traffic demand data (from the NC Ferry Division), and (3) operational and capital expenditures (from the NC Ferry Division). The research team also plans to use cell phone data to validate survey findings and acquire additional economic insights. The final deliverables will include a route profile of traffic data and economic contribution for each of the routes, based on passenger spending/economic benefit derived from their use of the ferry route and the operational and capital expenditure analysis of the system.]]></description>
      <pubDate>Tue, 29 Sep 2026 14:56:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2781131</guid>
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    <item>
      <title>Modeling Preferences for Autonomous Vehicle Access Types and Usage Intentions</title>
      <link>https://rip.trb.org/View/2780202</link>
      <description><![CDATA[As autonomous vehicle (AV) technologies continue to advance, it is increasingly important for transportation agencies to understand how individuals will access and use these technologies, as different access models have substantially different implications for travel demand, system performance, and infrastructure investment. This project examines preferences across two primary AV access modes, private ownership and ridehailing-based services, and analyzes how these preferences relate to anticipated AV usage behaviors. The study considers three key usage dimensions: willingness to use AVs for transporting children, anticipated in-vehicle time use, and willingness to relinquish driving control. In addition, the analysis incorporates three individual-level constructs that influence AV-related decisions, including perceptions of AV safety, prior experience with advanced vehicle technologies, and affinity toward shared mobility. Using data from the 2024 California Vehicle Survey, the project develops an integrated behavioral modeling framework that jointly represents AV access preferences and usage intentions. The results will provide actionable insights for improving travel demand forecasting, supporting infrastructure and fleet planning, and informing policy strategies related to AV deployment, system operations, and long-term transportation system performance.]]></description>
      <pubDate>Fri, 18 Sep 2026 10:17:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780202</guid>
    </item>
    <item>
      <title>Advancing Accessible Bus Transit: Human-Machine Interfaces for Inclusive Communication and Safe Boarding of Passengers with Disabilities</title>
      <link>https://rip.trb.org/View/2778268</link>
      <description><![CDATA[This research focuses on co-designing and testing innovative rider communication tools to enable passengers with disabilities to effectively signal their intent to board public bus transit. People with disabilities often face challenges in visually or physically signaling their needs at bus stops, leading to missed rides, inadequate assistance, and reduced confidence in transit systems. By developing human-machine interfaces (HMIs) that facilitate clear communication between riders and drivers, this project aims to improve access to public transit.

Inspired by advancements in V2X communication, the U.S. Department of Transportation has demonstrated the potential of rider-to-vehicle communication through initiatives like the Integrated Dynamic Transit Operation System (IDTO) (Meng et al. 2018). In this system, riders could use an app to request connection protection, relayed to drivers via dispatchers, reducing wait times at transfer points by an average of seven minutes. Similar systems, such as flex-routing, allow riders to request pickups near fixed routes, addressing access in rural areas (e.g., Skagit Transit). While these approaches demonstrate the value of pre-trip communication, they primarily target specific scenarios and do not fully address the needs of passengers with disabilities, particularly those who cannot visually discern approaching buses or signal drivers effectively (Greer et al. 2014, Steinfeld et al. 2019).

To address these gaps, the research team will conduct participatory design workshops with transit riders, drivers, and advocacy groups to guide the creation of rider communication systems. These workshops will provide collaborative spaces for sharing experiences, identifying unmet needs, and developing solutions through activities like brainstorming, low-fidelity prototyping, and role-playing real-world scenarios. Insights from these sessions will inform functional prototypes, such as mobile apps or physical interfaces at bus stops. Simulated experiments will evaluate the impact of these tools on driver workload, operational safety, and rider satisfaction. These experiments will also assess how HMIs help drivers assist passengers while minimizing distractions and maintaining safety standards. For example, the team will analyze how notifications via mobile apps or visual indicators at bus stops can aid drivers in identifying and assisting riders while avoiding information overload that could compromise situational awareness. Additionally, by accounting for various scenarios—such as varying weather conditions, crowded bus stops, and different disabilities—the tools will aim to address real-world challenges. The team will also evaluate the timing and content of communications to optimize their effectiveness for drivers and riders alike.

This participatory approach seeks to create communication systems that reduce barriers to mobility for passengers with disabilities, ensuring access to public transit. This research contributes to the broader goal of building safer transit systems.]]></description>
      <pubDate>Fri, 18 Sep 2026 10:09:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778268</guid>
    </item>
    <item>
      <title>Update of ACRP Report 4: Ground Access to Airports by Public Transportation</title>
      <link>https://rip.trb.org/View/2772536</link>
      <description><![CDATA[Airport operators continue to strengthen public transportation access for passengers and employees to enhance customer experience, support regional mobility goals, expand access to airport employment opportunities, reduce roadway congestion, and improve the environmental performance of the transportation system. The Airport Cooperative Research Program (ACRP) has produced several reports documenting strategies for improving public transportation access to airports, including ACRP Report 4 and related Cooperative Research Program publications. Since these reports were published, the transportation landscape has evolved with the emergence of transportation network companies and shared micromobility services, advancements in autonomous vehicle technologies, expanded rail connections at many U.S. airports, and new approaches to data collection and analysis. In addition, current airport transit ridership and passenger and employee mode share data are needed to support benchmarking, planning, and decision-making. Research is needed to update the existing body of knowledge, document current practices and emerging approaches, and compile current airport transit ridership and passenger and employee mode share data. The objective of this research is to update the strategies and information contained in ACRP Report 4 and related Cooperative Research Program reports, with a focus on airport passenger and employee mode shares and bus and rail transit ridership. The research should document current transit strategies, practices, and lessons learned; examine relevant changes and emerging approaches in airport ground access; and compile and analyze current airport transit ridership and passenger and employee mode share data using current survey methods and other available data sources. The resulting guidance should address airports of varying sizes and operating environments and be designed for use by airport planners and operators, transit agencies, regional planners, and other stakeholders involved in improving public transportation access to airports.  ]]></description>
      <pubDate>Thu, 03 Sep 2026 08:39:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772536</guid>
    </item>
    <item>
      <title>Conduct Economic Impact Assessment of Transit in Texas</title>
      <link>https://rip.trb.org/View/2767431</link>
      <description><![CDATA[The project will conduct a comprehensive economic impact and value analysis of transit agencies operating in state funded areas of Texas and key stakeholders deriving benefit from transit. This analysis will quantify how public transportation supports economic growth and contributes to employment, wages, and business activity across the state's diverse transit systems. To ensure full statewide representation, the assessment will include metro, urban, and rural transit agencies, allowing comparison of economic outcomes across different geographic and operational contexts. Equally important, the project will evaluate nontraditional, social, and environmental impacts of transit. These include access to healthcare, improved livability, environmental benefits, and other quality of life enhancements that reflect the broader value of transit to communities and key stakeholders including employers, educational institutions, veterans services, and healthcare providers. The work will build on the ongoing efforts at the Texas Department of Transportation (TXDOT) to expand performance measures that capture a more complete picture of transit benefits. The project will also incorporate benefits experienced directly by riders, such as value of time savings, cost savings, and other user level advantages that contribute to the overall value of transit services. Finally, the project will assess correlations between ridership levels and observed economic impacts to help clarify how transit usage translates into economic outcomes. Together, these objectives provide a holistic evaluation of transit's economic value to Texas.]]></description>
      <pubDate>Thu, 27 Aug 2026 11:25:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2767431</guid>
    </item>
    <item>
      <title>Potential Impact of Autonomous Vehicles on Reducing Congestion - Phase 2</title>
      <link>https://rip.trb.org/View/2733194</link>
      <description><![CDATA[Traffic congestion is a major problem in large metropolitan areas in the United States. In 2022, on average, a commuter lost about $1,259 in monetary terms annually due to congestion nationwide, which amounts to 8.7 billion lost hours in total.  The lack of coordination among individual users, who make routing decisions independently based on current traffic information without anticipating that others may follow similar decision-making patterns, contributes significantly to the high cost of congestion.    

The behavior of drivers optimizing their individual routes leads to a state known as the User Equilibrium, leading to travel times that can be significantly higher than travel times from the System Optimal, particularly in congested urban networks where the effects of individual decisions cascade throughout the system.  With the future emergence of autonomous vehicles, it is possible that organizations may now own more of the fleet of vehicles and control their routing, providing the organization more options for balancing route selections and thus making it possible to find routing solutions closer to the system optimal.  Driverless ride-hailing companies such as Waymo have already begun their service in five major cities across the United States and Tesla has started to test their Robotaxi service in Austin, Texas.

In Phase 1, the research team developed the research foundation for this problem. This work includes the literature review and the development of an online dispatch-and-relocation framework for a centrally controlled autonomous vehicle fleet. The Phase 1 framework matches requests to vehicles while accounting for pickup deadlines, near-term vehicle availability, and proactive repositioning toward forecasted demand. Phase 1 also establishes a comparison structure against a traditional human-driver ride-hailing system and an initial simulation capability that traces routes and estimates vehicle miles traveled, deadhead miles, passenger waiting time, revenue, and related performance measures.

Phase 2 will build directly on this foundation and is the primary focus of the next stage of the project. In Phase 2, the team will scale the optimization and simulation framework so it can solve problems at the size of major metropolitan areas. This includes extending the model to larger networks and richer demand patterns, improving computational tractability for larger instances, and strengthening the simulation module so it can evaluate passenger-vehicle matches and route decisions under more realistic operating conditions. To make the model scalable, the team will aggregate the service region into zones and solve the resulting problems repeatedly over short rolling horizons. The team will also need to calibrate the demand forecasting and routing inputs for large urban networks and test the algorithms on progressively larger instances to ensure that the solution quality and computation time remain practical. The purpose of Phase 2 is to determine how much centralized control of autonomous fleets can reduce system-wide travel, deadhead mileage, waiting times, and congestion when evaluated on realistic metropolitan-scale settings.

]]></description>
      <pubDate>Wed, 22 Jul 2026 17:37:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2733194</guid>
    </item>
    <item>
      <title>UAM-enabled Multimodal Analysis of Transportation Systems for LA28 and beyond: Phase 2</title>
      <link>https://rip.trb.org/View/2733110</link>
      <description><![CDATA[The Los Angeles region has long been projected as a testing ground for urban air mobility, comprised of air taxis and drone delivery, given the region’s favorable climate, traffic problems, and tech-savvy ecosystem. The LA28 Olympic and Paralympic Games present an opportunity to make such a testing ground a reality. Phase 1 of this project was focused on developing optimal dispatch algorithms for operation of a network of air taxis during LA28 and thereafter. The algorithms were used to develop supply models for the air taxi mode which was then utilized to study the resulting mode shift from other ground-based modes of transportation. The algorithms and models developed in Phase 1 utilized low resolution, and hence, conservative safety considerations for operations. Furthermore, the current dispatch algorithms do not explicitly account for other air traffic constituents, such as delivery drones, or commercial air traffic, which could interfere with the operation of air taxis especially around takeoff and landing sites. Phase 2 will refine the dispatch algorithms to account for such practicalities, thereby making local air transportation more efficient and reliable. These refinements will naturally lead to more accurate supply models for air taxis, which will then lead to more accurate assessment of mode shifts away from ground-based transportation. The phase 2 research is specifically aimed at viability of air taxi service beyond LA28 and the implications for mode shifts from ground transportation modes, when air mobility will potentially be integrated within the regional mobility network]]></description>
      <pubDate>Wed, 22 Jul 2026 17:32:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2733110</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>Lessons Learned on Mobility as a Service (MaaS): Exploring Opportunities and Barriers for the U.S. Context</title>
      <link>https://rip.trb.org/View/2696846</link>
      <description><![CDATA[Mobility as a Service (MaaS) packages can increase the popularity of alternatives to owning (and using) a personal vehicle, through the integration of multiple transportation services and options on the same platform. Several MaaS solutions have been proposed in Europe and other regions of the world. However, there is a dearth of research on MaaS in the US context. This study will be a starting point to fill that gap. In this study, the researchers will review MaaS experience from abroad and investigate the lessons learned on the way MaaS works, the various levels of integration possible on the MaaS platform, the type of transportation services that are offered, and the way (bundle) payments and fare integration are handled. The study will then build US-specific knowledge on the potential attractiveness of MaaS-type mobility packages through hosting focus group discussions with groups of travelers, to identify their potential openness to adopt MaaS services, the perceived benefits that would be derived from their use, and the characteristics that MaaS solutions should have to (eventually) be attractive among selected groups of US travelers. The findings from this study will help understand what realistic paths may exist to integrate public transit and shared mobility solutions to expand travel options in the US, and how effectively these options might encourage travelers to increase travel multimodality and reduce their reliance on the use of private vehicles. This study will serve as a starting point for developing future larger studies on MaaS in the US context.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:03:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696846</guid>
    </item>
    <item>
      <title>Passenger Evacuation Experience</title>
      <link>https://rip.trb.org/View/2694524</link>
      <description><![CDATA[Data and information on passenger perspectives from recent aircraft evacuations are required to identify areas of success and needs for improvements. During the normal investigation of these incidents, the response rate to the office inquiry is low, and the National Transportation Safety Board (NTSB) does not collect these data points unless it is classified as an accident. These data are needed to evaluate passenger behavior, passenger flow,  experience with passengers with disabilities and children, and evaluate decision making on passengers choosing to take personal items with them. Alternate means of reaching out to passengers will be investigated to gather these data. Ensuring to document any challenges the passengers faced, points of confusion, injuries sustained, or aspects which made the process operate efficiently. Special focus will be on parents/caregivers traveling with children, passengers who have special accessibility needs or physical limitations, those traveling with animals, and those who evacuated with personal items. Research team will collect similar responses from cabin crew members, such as flight attendants, to gain their perspective on the evacuation event and areas for possible improvement. These data collections were recommended as part of the Emergency Evacuation Aviation Rulemaking Committee (ARC). ]]></description>
      <pubDate>Wed, 22 Apr 2026 10:30:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2694524</guid>
    </item>
    <item>
      <title>Reducing empty miles of shared mobility on highway corridors</title>
      <link>https://rip.trb.org/View/2691661</link>
      <description><![CDATA[Smartphone-app-based technology has provided business opportunities to various demand-responsive urban transportation services, including e-hailing taxis, ride-pooling, and microtransit. These shared mobility services exhibit great potential for enhancing transportation services in rural communities. A common side effect, however, is a substantial portion of empty vehicle miles traveled (VMT) on highway corridors, which induces further congestion to highway traffic in peak hours. A quantitative analysis tool is necessary for planning agencies and policymakers to assess the impact of shared mobility on highway traffic. The researcher's recent work investigating ride-pooling systems serving uniformly distributed demands in a single community shows that their efficiency is highly sensitive to online matching schemes. This impact is expected to be even more significant in spatially imbalanced demand patterns, such as those between suburban/rural communities. This project will develop a traffic assignment model to allocate vehicular trips to corridor networks linking suburban and rural communities, which will assist policymakers in (1) understanding the relations between the spatial distribution of inter-community travel demands and excessive VMT; (2) identifying the most vulnerable corridors affected by shared mobility services; and (3) evaluating the potentials of various regulatory policies and public surcharges in reducing empty vehicle mileage. Ultimately, the analysis tool will enable planning agencies to explore practical measures to improve the accessibility of suburban and rural communities with shared mobility services.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:16:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691661</guid>
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