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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>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>
    <item>
      <title>Enabling Mobility for Older Adults in the US</title>
      <link>https://rip.trb.org/View/2669552</link>
      <description><![CDATA[Driving is essential for the preservation of functional independence for older adults, yet there is a growing number of older adult drivers with comorbid health conditions that might impair their ability to drive safely. Older adult drivers are overrepresented in motor vehicle crash deaths and injuries, which is a major public health concern. The purpose of this project is to (1) develop a comprehensive understanding of the mobility needs and challenges of older adults in the United States, and (2) develop an innovative tool to extend their safety while they drive.  

Aim 1: Develop a comprehensive understanding of the mobility needs and challenges of older adults  

To develop a comprehensive understanding of the mobility needs of older adults, the research team will analyze data from a nationally representative survey of U.S. adults aged 65 and older. The survey contains a comprehensive set of questions that explore driving behavior, transportation options, mobility limitations, and attitudes toward future transportation technologies and policies among adults aged 65 and older.   

Aim 2: Develop an innovative tool to extend their safety while they drive.  

The goal of this project is to understand older adults’ perceptions of an app (StreetCoach) that provides a driving score based on actual driving behavior. A number of apps exist for older adult drivers but the perceptions of older drivers towards their driving score is poorly understood. This study will use a sequential explanatory research design by asking 10 older adults to download and use the app for 60 days. Following this, the research team will conduct in-depth interviews with the participants to gain an understanding of their perception and interpretation of their telematics score, and what factors might motivate them to change their driving to improve the score.   ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:16:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669552</guid>
    </item>
    <item>
      <title>Impact of Passing Lane Guidance through Angled Markings on Two-Lane Rural Highways</title>
      <link>https://rip.trb.org/View/2655580</link>
      <description><![CDATA[Previous research has shown that the provision of low-cost measures, such as passing lanes, can be highly cost-effective in improving the level of service of two-lane highways, by increasing passing opportunities and safety. The passing lanes, such as Super 2 highways adopted by Texas Department of Transportation (TxDOT) are beneficial in dispersing platoons at locations where passing sight distance is shorter than the designated passing zones.
However, drivers should be informed, educated, and receptive to such design changes to have any positive impact on driving behavior and safety on these highways. In addition, the adopted pavement markings or design features used along passing lanes should be intuitive and considerate of human factors. Therefore, it is essential to investigate drivers’ perception and behavioral response to design changes in the passing lanes, such as any transitional lane markings, to ensure the desired safety and operational benefits prior to the installation at selected sites.]]></description>
      <pubDate>Thu, 15 Jan 2026 13:01:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655580</guid>
    </item>
    <item>
      <title>Impact of Double Centerline Rumble Strips (CLRS) on Driver Behavior</title>
      <link>https://rip.trb.org/View/2655579</link>
      <description><![CDATA[According to the NCHRP report 641 (2005) Centerline rumble strips (CLRS) effectively reduce head-on, sideswipe, and crossing-the-centerline crashes on two-lane rural highways by 44-46%. Benefits of using CLRS include alerting the inattentive drivers, offering visibility of lane markings, and providing a low-cost treatment to avoid crashes. Previous research suggests that a separation of 6” between the double rumble strips along the center of the road can prevent joint deterioration. In addition, the gap may alert the driver to allow sufficient time to correct lane encroachment. However, the effectiveness of such a gap between double CLRS to enforce safety has not been investigated. Furthermore, other factors may affect the effectiveness of double CLRS on surrogate safety measures, such as traffic density, visibility conditions, geometric design (crest curves), presence of shoulders (paved/unpaved), passing zones, etc. Therefore, the objective of this project is to investigate the impact of different double CLRS installation patterns on the behavior and safety of drivers. To achieve this objective, a driving simulator-based study will be designed to investigate the effect of the pattern of double CLRS on driving behavior using various surrogate safety measures such as lane position, speed, post lane encroachment time, and time-to-collision (TTC). These measures will be collected across segments installed with and without the double CLRS in a control condition without any distraction/inattentiveness and compared with the test conditions including a distracted driver using a driving simulator experiment. Based on the study findings, the final report will include recommendations for the systematic installation of double CLRS for safety enhancement.]]></description>
      <pubDate>Thu, 15 Jan 2026 12:52:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655579</guid>
    </item>
    <item>
      <title>Healthy Micromobility: Moving From Crisis to Opportunity</title>
      <link>https://rip.trb.org/View/2652680</link>
      <description><![CDATA[Micromobility, including e-scooters and e-bikes, is an emerging transportation mode with the potential to alleviate congestion and improve urban mobility. However, prior research has primarily focused on safety risks and injury rates, with less attention given to its potential benefits, such as improved accessibility, reduced vehicle miles traveled (VMT), and enhanced health through active transportation. This project aims to provide a more comprehensive assessment of both the risks and benefits of electric micromobility within the U.S. transportation system using a combination of literature review, survey research, and systems dynamic modeling. The study examines how electric micromobility reduces VMT while also evaluating the health trade-offs related to safety risks and active transportation benefits. The project consists of three main aims: (1) a targeted literature review to synthesize existing evidence on electrified micromobility’s health impacts, (2) a nationally representative survey to capture user behavior, trip substitution patterns, and safety concerns, and (3) the development of a system dynamics simulation model to quantify the net health effects across diverse urban settings.     ]]></description>
      <pubDate>Tue, 13 Jan 2026 16:27:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652680</guid>
    </item>
    <item>
      <title>Measuring and Modeling Safety &amp; Congestion Impacts of Double Parking</title>
      <link>https://rip.trb.org/View/2643032</link>
      <description><![CDATA[Double parking is a common response to limited curb availability in dense urban areas and contributes to traffic disruptions, conflicts among roadway users, and increased crash risk. Despite its prevalence, the safety and congestion impacts of double parking are not well quantified, and cities lack analytical tools to evaluate potential policy interventions. This project addresses these gaps through integrated data collection and modeling.

The research will combine video-based field observations, surveys of commercial drivers, and crash and enforcement data to characterize double-parking behavior and its effects on traffic flow and safety. Behavioral choice models will be developed to estimate the likelihood of double parking versus cruising under varying curb availability and policy conditions. By linking curb use decisions with safety and congestion outcomes, the project will provide quantitative tools to evaluate curb management strategies and support more effective urban transportation policies.]]></description>
      <pubDate>Thu, 18 Dec 2025 15:01:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643032</guid>
    </item>
    <item>
      <title>Deskilling or Redefining? Drivers’ Hazard Anticipation with Automation</title>
      <link>https://rip.trb.org/View/2643018</link>
      <description><![CDATA[Hazard anticipation is a critical skill for crash avoidance, requiring drivers to detect, predict, and respond to potential roadway threats. As Advanced Driver Assistance Systems (ADAS) become more common, drivers increasingly shift from active vehicle control to supervisory roles, which may change visual scanning patterns, situational awareness, and response strategies. These changes are not yet fully understood and may have important safety implications.

This project investigates how automation affects hazard anticipation by examining driver gaze behavior, responses to traditional roadway hazards, and reactions to system-related cues in ADAS-equipped vehicles. The research integrates secondary data analysis, development of a hazard anticipation taxonomy, and experimental testing using a high-fidelity driving simulator. Results will be used to identify automation-related changes in hazard anticipation and to develop targeted training approaches aimed at maintaining effective driver oversight. Findings from this work will support safer human interaction with automated vehicle technologies as their use continues to expand.]]></description>
      <pubDate>Thu, 18 Dec 2025 14:13:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643018</guid>
    </item>
    <item>
      <title>SAFE Schools For Safer Future
</title>
      <link>https://rip.trb.org/View/2628212</link>
      <description><![CDATA[Seatbelts Are For Everyone (SAFE) is a Kansas state initiative program launched in 2008 by DCCCA Inc. (Developing Caring Communities Committed to Action) to increase teen restraint compliance through education, positive reinforcement, and enforcement. This teen-led, peer-to-peer program aims to reduce the number of teen motor vehicle injuries and fatalities. The SAFE program has been a component of the Kansas Traffic Safety Resource Office (KTSRO) for over a decade. The program was expanded to Oklahoma and Missouri in 2014 and 2016 respectively, and Iowa adopted the program in 2021. 
The goal of the current research project is to understand the efficacy of the SAFE program through a multifaceted approach. This project will be conducted in two parts; the first part includes surveying high school students on the topics of seatbelt, traffic laws and other safe driving behaviors covered by the SAFE program that can help in understanding their attitudes, perceptions, knowledge and experience regarding road safety and how it differs between students participating SAFE and non-SAFE schools. In addition, this part will also include socioeconomic analysis to determine the influence of equity factors on the driving behaviors and perception of Kansas teen drivers. The second part of the study will include conducting literature review, examining Kansas fatal and serious injury crashes involving teens over a 13-year period, from 2010 to 2023, and their potential contributing factors, identifying various safety programs, best practices and initiatives across the nation focused on improving teen safety and emphasizing the importance of educating and training young drivers during their early driving phases. It is critical to instill safe driving behavior for young people from an early age to foster a safety culture and better mobility in the future.
]]></description>
      <pubDate>Fri, 21 Nov 2025 14:27:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2628212</guid>
    </item>
    <item>
      <title>ELDER-3: Empowering Lifelong Driving Experiences with SAE Level 3 Automation
</title>
      <link>https://rip.trb.org/View/2628204</link>
      <description><![CDATA[Automated vehicles (AVs) are heralded as the future of transportation. The Society of Automotive Engineers categorizes six levels of AV, with levels 1 and 2 already in operation, and level 3 undergoing mass production testing in North America. Level 3 marks a significant advancement, as drivers can engage in non-driving related tasks (NDRT), but must be prepared to take over control of the vehicle at all times. However, level 3 AVs pose significant cognitive and motor demands during take-over requests (TOR) in older drivers with intact cognition, suggesting that take-over maneuvers may be even more challenging for older adults with cognitive impairment (CI).
This study aims to determine the impact of CI on take-over performance in level 3 AVs. Participants (30 older drivers with intact cognition, and 30 older drivers with CI) will engage in level 3 AV driving using a high-fidelity driving simulator while their eyes are tracked for attention and cognitive workload. During the drive, the TOR will require participants to quickly transition from an NDRT to taking over control of the vehicle. Additionally, participants will complete a clinical battery of cognitive, visual, and motor tests.
We hypothesize that older adults with CI will exhibit: (1) slower response to TOR; (2) reduced attention (glances on screen) and increased drowsiness (eyelid closure) before TOR; and (3) heightened cognitive workload (pupillary response) during and after the TOR. In hypothesis (4), we expect that a combination of clinical tests including reaction time, processing speed, and working will predict take-over performance.
]]></description>
      <pubDate>Fri, 21 Nov 2025 14:20:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2628204</guid>
    </item>
    <item>
      <title>Identifying Harsh Driving Behaviors and Contributing Factors Using Telematics Data: A Case Study in Oakland and Fresno, California</title>
      <link>https://rip.trb.org/View/2625583</link>
      <description><![CDATA[Despite extensive safety countermeasures, vulnerable road users continue to face significant risks on urban roadways, resulting in a substantial loss of life. Safety frameworks like Vision Zero and the Safe System Approach call for proactive solutions that address these dangers before severe crashes occur. This proactive approach can be powered by surrogate safety measures, which use data on near-misses and risky behaviors to identify hazards. Harsh driving events—such as harsh braking or acceleration—serve as excellent indicators of elevated crash risk. These behaviors are influenced by a combination of factors, including roadway design, traffic flow, and the complex, unpredictable interactions between vehicles and other road users in dense urban environments. This study leverages high-resolution telematics data from the Cities of Oakland and Fresno to investigate the differential impacts of harsh driving behaviors on road safety.  We will construct and compare crash hotspots (e.g., high injury network) and harsh driving behavior hotspots to examine which types of harsh driving behaviors most strongly align with crashes involving vulnerable road users and latent crash risks. Additionally, by using statistical methods and explainable artificial intelligence techniques, we will analyze roadway characteristics (e.g., intersections, lane curvature, or slope), as well as traffic flow and surrounding conditions, to determine whether specific features are associated with increased prevalence of harsh driving events that, in turn, elevate crash risk.  By integrating the spatiotemporal patterns of crashes and telematics-based behavioral measures, along with infrastructure characteristics, this study aims to better understand how risky driving patterns contribute to vulnerable road user safety outcomes. The findings will provide actionable insights for prioritizing enforcement such as speed camera deployment, designing infrastructure countermeasures, and developing data-driven, proactive strategies to support safe transportation.]]></description>
      <pubDate>Tue, 18 Nov 2025 15:33:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625583</guid>
    </item>
    <item>
      <title>Are Automonous Vehicles Safer Drivers than Humans? Comparing performance in San Francisco</title>
      <link>https://rip.trb.org/View/2625584</link>
      <description><![CDATA[This research project seeks to determine if automated vehicles (AVs) are safer drivers than humans by comparing their pedestrian interaction behaviors and yielding performance in real-world conditions in San Francisco. The study will be framed by the city's "Focus on Five" strategy, which targets the five moving violations most commonly associated with traffic fatalities. Researchers will conduct evaluations of two focus violations, with the first being a comparison of the compliance of AVs and human drivers in yielding to pedestrians in a crosswalk. To gather data, the team will install high-resolution video cameras at two or more crosswalks with no traffic control for a period of one to three weeks to passively record vehicle-pedestrian interactions. Machine learning-based computer vision methods will then be used to automatically classify vehicles as either automated or human-driven. Following this classification, researchers will review the footage to code each interaction, noting if the vehicle yielded to the pedestrian. Finally, the performance of the two groups will be compared using two-sample t-tests to determine if any observed differences are statistically significant. A parallel analysis will be conducted for a second violation, to be determined.]]></description>
      <pubDate>Tue, 18 Nov 2025 15:14:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625584</guid>
    </item>
    <item>
      <title>Exploring the Influence of Tinted Windows and Other in-vehicle Distractions on Driver Detection of Pedestrians and Bicyclists</title>
      <link>https://rip.trb.org/View/2625589</link>
      <description><![CDATA[This project seeks to address increasing road fatalities in the United States, especially among pedestrians and bicyclists, by investigating how vehicle features such as tinted windows and in-vehicle screens impact driver visual perception and the safety of vulnerable road users. The scope centers on visibility-related safety issues near intersections and the potential added risks posed by heavily tinted windows, which may hinder both driver detection of pedestrians and cyclists and police or witness identification. The objectives are to experimentally evaluate the effect of varying window tint levels and screen use on driver attention, detection ability, and overall safety in low-light and low-contrast conditions. The method involves controlled studies using advanced eye-tracking technology to measure driver performance and behavior across multiple tint percentages, ultimately informing safer vehicle and roadway practices for non-motorized users that align with the Safe Systems Approach.]]></description>
      <pubDate>Mon, 17 Nov 2025 16:54:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625589</guid>
    </item>
    <item>
      <title>Benefit-Cost Methodology for Behavioral Highway Safety Countermeasures</title>
      <link>https://rip.trb.org/View/2611416</link>
      <description><![CDATA[States are facing increasingly difficult decisions on budget expenditures and programs. Currently there is no nationally recognized methodology to assist the states in allocating safety resources among behavioral safety countermeasures. NCHRP Report 622:  Effectiveness of Behavioral Highway Safety Countermeasures created a classification scheme to estimate the effectiveness of countermeasures. A benefit-cost methodology would assist states in making investment decisions concerning behavioral safety countermeasures and provide opportunities to compare the value of behavioral and engineering countermeasures side-by-side. NCHRP Report 622 identified 34 countermeasures that have been “proven” effective, 54 countermeasures whose effectiveness is rated as “unlikely/uncertain or unknown,” and 13 countermeasures believed “likely” to work but for which evidence of effectiveness is not conclusive. Research is needed to develop a widely usable methodology to determine benefit and cost for behavioral countermeasures. Additional research is also needed to advance the state of knowledge on the effectiveness of behavioral countermeasures that are “likely” to work.
 
OBJECTIVES: The objectives of this research are to:
 
(a) Develop a benefit-cost methodology for behavioral highway safety countermeasures that can be used by state and local entities. The methodology should provide a quantitative analytical approach that uses clearly defined criteria to determine the value of the countermeasure. It should also include an approach for isolating the effects of individual countermeasures. Costs should include specific state and local program implementation costs, other costs borne by government, and societal costs (e.g., private medical costs, lost wages, reduced productivity).
 
(b) Apply the methodology to at least three proven (known effectiveness) countermeasures to demonstrate that the methodology is effective and widely usable.  The proven countermeasures should come from the areas of occupant protection, alcohol/drug impairment, and speed. Revise the methodology as needed.
 
(c) Once the benefit-cost methodology is successfully used (objective “b”), apply it to three to five of the countermeasures rated as “likely” to be effective (see NCHRP Report 622).  This is a two-part process: (1) quantify the effectiveness and ( 2) apply the methodology to determine the benefit-cost of the countermeasure.
 
]]></description>
      <pubDate>Tue, 21 Oct 2025 15:51:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2611416</guid>
    </item>
    <item>
      <title>A Novel Red Light Running Warning System Using Connected v2i Technology, Phase 2: Driver Testing on Public Roads</title>
      <link>https://rip.trb.org/View/2487386</link>
      <description><![CDATA[The purpose of this project  (which follows up on the research described in “Development and Demonstration of a Novel Red Light Running Warning System Using Connected v2i Technology”) is to develop red light running warning system (RLRWS) technology towards general driver use. The research team will resolve three main limitations with the prototype work. 1) The team will test responses by drivers who were not involved with engineering the RLRWS towards use by the general public. 2) The research team will implement an iterative learning control to adapt the RLRWS warning to the individual driver so that the driver’s response matches the computed desired response. 3) The prototype testing will occur only at a single intersection. The research team will create a loop of 5 intersections with varying geometries to create more options for testing the RLRWS. By expanding the RLRWS to multiple intersections with more drivers, the research team has the opportunity to demonstrate its real-world impact and pave the way for broader adoption, ultimately making Minnesota's roads safer for all.]]></description>
      <pubDate>Wed, 08 Oct 2025 10:16:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487386</guid>
    </item>
    <item>
      <title>Quantifying the Influence of Driver Behavior on Greenhouse Gas Emissions and Air Quality</title>
      <link>https://rip.trb.org/View/2606557</link>
      <description><![CDATA[This project will study how both greenhouse gas (CO2) and pollutant (NOx and particle) emissions of the Minnesota vehicle fleet compare to modeled values to quantify the increase in emissions attributable to driver behavior. A very large time-series dataset of over 1M vehicles owned by the University of Minnesota will be used to study real-driving cycles on Minnesota roads. Emissions factors from laboratory measurements and from literature will be used to estimate the increase in emissions over those predicted by models like the EPA-MOVES framework. On-road emissions will also be measured using gas and particle instruments mounted in a chase vehicle equipped with radar sensing to correlate emissions to local traffic speed and acceleration, a novel approach differing from conventional fixed site measurements. Measurements will also identify vehicles with higher-than-expected emissions due to poor maintenance and tampering and quantify their impact on overall pollution.]]></description>
      <pubDate>Fri, 03 Oct 2025 15:02:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606557</guid>
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