<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Assessing Traffic Flow and Motorcyclist Safety Outcomes Associated with Lane Filtering</title>
      <link>https://rip.trb.org/View/2720302</link>
      <description><![CDATA[There is a significant gap in research examining how motorcyclists and drivers behave when motorcyclists lane split and/or lane filter in live traffic environments, and safety outcomes associated with lane splitting and lane filtering.

Motorcycle lane splitting involves riding between lanes of slow-moving or stopped traffic.

Lane filtering is the practice of a motorcyclist moving between lanes of stopped or slow-moving traffic to move to the front of the queue, typically at intersections or during heavy congestion.

These practices are legal in some states, prohibited in others, and ambiguously defined in others. Motorcycle safety advocates and motorcyclist rights groups frequently cite safety benefits associated with lane splitting and lane filtering, while law enforcement agencies and traffic safety organizations raise concerns regarding increased risk and operational conflicts. These discussions often rely on limited domestic research, most notably a single observational study conducted in California, as well as studies conducted outside the United States. International research provides useful context but is limited in its applicability to U.S. traffic environments.

U.S. decision-makers are often required to extrapolate from non-comparable data sources, contributing to inconsistent definitions and interpretations of these practices. Developing a better understanding of real-world interactions between motorists and motorcyclists, and safety outcomes associated with motorcycle lane splitting and lane filtering is critical for the development of effective driver training curricula and public education campaigns.

The objectives of this research are to: 1. Document the existing state of knowledge regarding the impact safety outcomes associated with motorcycle lane splitting and lane filtering; 2. To support development of standardized, behaviorally grounded definitions to reduce ambiguity and improve consistency in policy and practice, conduct a multi-state observational study to understand interactions between motorists and motorcyclists during lane-splitting and lane-filtering; 3. Develop an evidence-based toolkit for use by state highway safety offices, state driver licensing agencies, and state motorcycle safety programs to manage the behavioral and safety complexities of motorcycle lane splitting and filtering.]]></description>
      <pubDate>Thu, 02 Jul 2026 19:30:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2720302</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>Evaluating the Effectiveness of Drivers' Education Modules on Safety </title>
      <link>https://rip.trb.org/View/2714400</link>
      <description><![CDATA[There is mixed evidence as to the effectiveness of drivers’ education courses. It is also unknown whether drivers’ education can be used to train drivers on how to effectively use driving automation. The objective of this research is to examine: How differences in drivers' education program delivery affects novice drivers' behavior, crashes, and citations within 12 months of licensure; How differences in novice drivers' pre-license behaviors affect crashes and citations within 12 months of licensure; How driver education and training programs can help improve novice drivers' use and understanding of advance driver assistance systems (ADAS).]]></description>
      <pubDate>Mon, 15 Jun 2026 15:59:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2714400</guid>
    </item>
    <item>
      <title>Low-Frequency High-Impact Travel for Data Analysis</title>
      <link>https://rip.trb.org/View/2709248</link>
      <description><![CDATA[Low-incidence travel behavior is difficult to capture in a traditional household travel study, where typically one to seven days of travel are collected from a representative sample of households. These behaviors may include travel modes used frequently by a small number of people (bicycling, carshare/vanpool), emerging modes not yet widely adopted (e-bikes, scooters, automated vehicles), complex household travel interactions, or infrequent behaviors such as rideshare use, long-distance travel, and trip replacement behavior such as home delivery of goods and services.

Because these behaviors occur infrequently, traditional survey methods often fail to collect enough observations for accurate estimation in travel demand models. A sufficient number of surveys—approximately 1,000 observations per market segment—is needed to support reliable analysis and forecasting. Despite their low incidence, many of these behaviors have significant impacts on transportation systems.

More than 40 state departments of transportation (DOTs) maintain statewide travel models that require accurate long-distance travel data to support costly intercity highway and rail investments. Emerging travel modes are also becoming critical policy issues in regional and statewide planning efforts.

The objective of this research is to identify and analyze methods for sampling people, households, and incidences of rare or emerging travel behaviors and determine how these methods can be incorporated into household travel survey data collection.]]></description>
      <pubDate>Tue, 02 Jun 2026 13:56:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709248</guid>
    </item>
    <item>
      <title>Agentic LLM-powered Synthetic Transportation Agent Response System (AL-STARS)</title>
      <link>https://rip.trb.org/View/2703689</link>
      <description><![CDATA[Household travel surveys help to gather data on travel trends and are a key part of transportation planning. Gathering this data has become challenging, however, as response rates have been decreasing. This project will develop a web-based travel survey tool (AL-STARS) that generates realistic, synthetic travel data. AL-STARS will use an advanced AI model to simulate travelers in Illinois, allowing planners and modelers to test transportation ideas and survey questions virtually before real-world use. The project will help IDOT make better decisions in infrastructure and policy by providing a more accurate, diverse and cost-effective way to understand how different communities — especially undersampled population groups — actually travel.]]></description>
      <pubDate>Fri, 15 May 2026 09:28:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703689</guid>
    </item>
    <item>
      <title>How do perceptions of transportation challenges influence travel behavior for people with disabilities?</title>
      <link>https://rip.trb.org/View/2702517</link>
      <description><![CDATA[Although it has been over thirty years since the Americans with Disabilities Act (ADA) was enacted, people with disabilities—who represent approximately one-quarter of the U.S. population—continue to face significant barriers to mobility and access. They tend to make fewer trips and are more reliant on others, largely due to shortcomings in pedestrian infrastructure, transit, and for-hire vehicle services, and specialized paratransit options, as well as the negative attitude of drivers to them. While substantial research has documented the wide range of mobility and access challenges faced by people with disabilities, there has been limited investigation into how these challenges affect their mode choice decisions. This project will develop and administer a web-based survey, targeting a sample of California residents with disabilities, to explore how disability shapes mode choice, factoring in perceptions of the inaccessibility of transportation infrastructure, mode design-induced challenges, and ableism faced while travelling by different modes. This project will explore how these problems influence their willingness to use paratransit and trip frequency to activity centers. The research team will apply various analytical techniques, including descriptive statistics, basic comparative statistical tests, and multinomial logistic regression, to address the research questions. To ensure the survey is relevant and impactful, the team will collaborate with organizations serving people with disabilities, both to inform the survey design and to disseminate findings that support broader universal access goals shared by these organizations and public agencies.]]></description>
      <pubDate>Thu, 14 May 2026 16:54:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702517</guid>
    </item>
    <item>
      <title>Exploring the Relationships Among Perceived Safety, Perceived Accessibility, and Travel Behavior</title>
      <link>https://rip.trb.org/View/2702636</link>
      <description><![CDATA[Accessibility, the potential to reach various opportunities that are spatially dispersed, is an important concept in transportation that has garnered an extensive amount of research. The methods of measurement of accessibility are numerous, and there has been no consensus on a singular best practice. Typical methods of measuring accessibility do not take into account individual differences. Perceived accessibility measures offer another approach to understanding individual differences inaccessibility. This project will contribute to perceived accessibility literature by conducting a study in the United States (U.S.) context in the state of California, as there have not been many studies conducted in the U.S. First, semi-structured interviews with adults residing in the Sacramento Area Council of Government (SACOG) region will be conducted. Next, a cross-sectional survey will be conducted using a sample of SACOG region residents. It is important to understand how perceptions of safety and accessibility may influence mode choice and ability to access economic opportunities.]]></description>
      <pubDate>Thu, 14 May 2026 16:47:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702636</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>Assessment of Litter Hot Spot Areas for Targeted Reduction in Prince George's County</title>
      <link>https://rip.trb.org/View/2701237</link>
      <description><![CDATA[The frequency and volume of litter and illegal dumping on state and county roadways in Prince George’s County are increasing, despite efforts like scheduled litter blitzes, which have shown limited long-term success. Over the past five years, the Maryland Department of Transportation State Highway Administration (MDOT SHA) spent approximately $42 million removing litter and debris, with last year’s costs alone reaching $15 million—the equivalent of 45 new dump trucks or nearly 60 miles of resurfaced roads (Source WBAL News: https://www.msn.com/en-us/news/us/drivers-watch-out-for-operation-clean-sweep-maryland/ar-BB1jY1rr). These expenditures are unsustainable, especially given recent fiscal shortfalls. This joint research proposal, submitted by District 3 and Prince George’s County Department of Public Works and Transportation (DPW&T), aims to evaluate litter hot spots at the census tract level, as current efforts have not addressed the root causes of the issue. Prince George’s County, a well-resourced and educated area, presents unique challenges, suggesting the problem extends beyond awareness or resource deficits. ]]></description>
      <pubDate>Wed, 13 May 2026 09:15:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701237</guid>
    </item>
    <item>
      <title>Investigating Driver Behavior Under Cyberattacks in Connected Vehicle Environments: Phase II</title>
      <link>https://rip.trb.org/View/2696964</link>
      <description><![CDATA[Phase II will examine driver behavior and decision-making under cyberattacks in connected-vehicle contexts using high-fidelity, human-in-the-loop driving simulators at Morgan State University (urban) and Clemson University (suburban). The team will develop reusable Cyberattack Injection Modules (CIMs) for UCWinRoads and SimCreator and validate one vehicle-centered attack (false blind spot warning) and two infrastructure-centered attacks (falsified signal phase-and-timing and “phantom” signal-ahead information). The study will leverage IRB-approved human-subject testing, conduct a Maryland MVA pilot demonstration, and curate a Standardized Attack Scenario Library (ASL) for replication and training use.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696964</guid>
    </item>
    <item>
      <title>Optimization-Based Framework and Decision-Support Tool for Bridge Toll Implementation Under Behavioral and Operational Constraints

</title>
      <link>https://rip.trb.org/View/2696158</link>
      <description><![CDATA[This project aims to develop a novel theoretical framework and a practical decision-support tool to guide strategic bridge toll implementation under real-world behavioral and operational constraints. Traditional toll optimization and project evaluation models focus on market uncertainties but neglect critical human behavioral factors, such as present bias, that significantly influence decision outcomes. To bridge this gap, the proposed research introduces an optimization framework that integrates behavioral dynamics into infrastructure decision-making, enabling the identification of strategies that maximize long-term social welfare while addressing short-term user response and implementation pressures. The accompanying decision-support tool will translate this framework into an interactive, user-friendly platform for transportation agencies and policymakers. It will allow users to simulate and compare alternative tolling strategies, assess implementation timelines, and visualize trade-offs between system efficiency, user response, and long-term performance outcomes. By empowering decision-makers to make data-driven, welfare-maximizing choices, this project supports more effective, publicly acceptable, and operationally robust tolling practices. The research will generate theoretical advances, peer-reviewed publications, and an actionable tool ready for integration with Florida Department of Transportation's (FDOT’s) planning processes, ultimately contributing to more resilient, safe and efficient transportation infrastructure systems.]]></description>
      <pubDate>Mon, 27 Apr 2026 19:59:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696158</guid>
    </item>
    <item>
      <title>Nanoscale Wear Mechanics-driven Durable Tire Design</title>
      <link>https://rip.trb.org/View/2696033</link>
      <description><![CDATA[The overall goal of this project is to establish a nanoscale, mechanics-based understanding of wear and fatigue processes that govern tire durability and service life, and to translate this understanding into design-relevant guidance for durable tire compound development. Tire durability and service life are governed by nanoscale mechanical damage processes that occur within tread compounds during repeated tire–road contact. These processes include crack initiation, viscoelastic fatigue, filler–polymer debonding, and localized energy dissipation. Conventional durability evaluations rely on bulk abrasion testing and full-scale wear trials, which provide performance rankings but do not resolve the mechanistic origins of material degradation. This project develops a mechanics-based approach to durable tire design by using atomic force microscopy (AFM) as a controlled nanoscale tribological tool to directly generate and measure wear under well-defined loading, shear, and temperature conditions. AFM enables direct observation and quantification of damage initiation at the scale where wear originates, allowing durability to be addressed at its physical root rather than through empirical correlation.]]></description>
      <pubDate>Sat, 25 Apr 2026 12:25:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696033</guid>
    </item>
    <item>
      <title>Disability, Mode Perceptions, and Travel Behavior</title>
      <link>https://rip.trb.org/View/2695812</link>
      <description><![CDATA[Despite the more than three decades since the passage of the Americans with Disabilities Act (ADA), people with disabilities, which comprise roughly one quarter of the US population, still face considerable challenges to their mobility and access. They make fewer trips and are more dependent on others because of deficiencies in pedestrian infrastructure, transit and for-hire vehicles, and specialized paratransit services. While there is a considerable amount of research that identifies the breadth of mobility challenges and access barriers, limited research has addressed how these mobility challenges influence mode choice for people with disabilities. This project will develop and administer a web-based survey by oversampling California residents with disabilities to understand how disability influences mode choice, accounting for perceptions of the built environment and mode-specific challenges. The research team anticipates using several analytical methods to answer the research questions, including descriptive statistics, basic statistical tests of comparison, and multinomial logistic regression. The research team aims to engage with disability-serving organizations to ensure that the survey reflects real concerns and will provide meaningful data, and to share results in support of universal access goals that the organizations and public agencies are pursuing.]]></description>
      <pubDate>Thu, 23 Apr 2026 17:58:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2695812</guid>
    </item>
    <item>
      <title>New Travel Insights from Cell Phone GPS Data</title>
      <link>https://rip.trb.org/View/2691672</link>
      <description><![CDATA[Building and operating an effective and efficient transportation system requires deep insights into how people move from place to place. These insights include long term behaviors to understand trips that happen infrequently, determining which routes people choose, and understanding how different demographics use their transit options. This project proposes to build an open-source software application programming interface (API) that will produce new travel insights from cell phone global positioning system (GPS) data. Currently available transportation data analytics packages omit important travel insights that are important for transportation research, such as long-term patterns of life, route selection, and demographically stratified travel behavior. These deeper analyses are important for planning more efficient transit. Abundant cell phone GPS data serves to replace costly travel surveys with better coverage and currency. However, GPS data is challenging due to noise, sporadic sampling, and privacy. Building on the research team lab’s extensive experience with GPS data, the researchers will create an open-source API to robustly deliver the new insights. Unlike commercially available packages, the API will be transparent and extensible. The API can serve as a platform for new algorithms and expanded insights. The project team will demonstrate the API on three National Center for Sustainable Transportation (NCST)-relevant mobility insights that are not possible with commercial packages: (1) Determining long term patterns of individual travel behavior, (2) detailed route selection for individuals, and (3) variations in travel behavior among different demographic groups.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:48:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691672</guid>
    </item>
    <item>
      <title>Sensor-informed Generative Digital Twin: High-fidelity Simulation for Sustainable Transportation and Policy Validation</title>
      <link>https://rip.trb.org/View/2691669</link>
      <description><![CDATA[Understanding the behaviors of vehicles and other traffic participants at busy urban intersections is critical for urban planning, infrastructure development, and policymaking. Unfortunately, such understanding often comes after a huge investment for implementation and deployment. Many complex interactions occur infrequently and are difficult to capture through after-deployment monitoring. This project will develop a sensor-informed generative digital twin that integrates real-world data from the Riverside Innovation Corridor’s sensor network. By continuously integrating real-time sensory inputs, the platform can be used to create high-fidelity scenarios and simulate rare and challenging transportation dynamics. The digital twin will serve as a decision-support tool for policy evaluation, traffic efficiency strategies, and urban mobility planning. Its predictive capabilities will assist in designing infrastructure for autonomous vehicles, optimizing multi-modal travel demand, and enhancing energy efficiency. Through engagement with policymakers and stakeholders, the project will pave the foundation for the digital twin’s application in real-world decision-making. The proposed research will serve as a bridge, connecting data-driven insights with policy implementation towards sustainable transportation systems.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:41:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691669</guid>
    </item>
  </channel>
</rss>