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    <title>Research in Progress (RIP)</title>
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    <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>
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      <title>Research in Progress (RIP)</title>
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      <title>AI Powered Conflict Detection and Signal Optimization for Right Turn FYAs in Mixed Modal Intersections</title>
      <link>https://rip.trb.org/View/2640185</link>
      <description><![CDATA[Right turn Flashing Yellow Arrows (FYAs) can support efficient traffic movement, but they also introduce uncertainty for drivers who must judge when to yield to pedestrians and cyclists. This uncertainty can increase the number of near miss interactions at mixed modal intersections. This project will create an artificial intelligence framework that uses video based detection to monitor turning vehicles, pedestrians, and cyclists in real time. The system will compute surrogate safety measures such as post encroachment time and time to collision to identify conditions that may increase the likelihood of a conflict.

The project will use these safety measures to support a signal timing optimization engine that balances safety with delay reduction. The research team will test the framework in simulation and explore opportunities for pilot deployment with the Connecticut Department of Transportation. The resulting tools will give agencies a practical method to assess right turn FYA performance, adjust timing plans when needed, and improve intersection safety through proactive conflict identification.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:35:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640185</guid>
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    <item>
      <title>Impact of Leading Pedestrian Intervals on All Users</title>
      <link>https://rip.trb.org/View/2608462</link>
      <description><![CDATA[A leading pedestrian interval (LPI) refers to a traffic signal phase wherein pedestrians receive the right-of-way prior to vehicular movements that are required to yield. During the remainder of the pedestrian phase, both vehicle and pedestrian movements may occur simultaneously. The Utah Department of Transportation (UDOT) signal timing team regularly receives requests to implement LPIs, which are traditionally considered straightforward measures to enhance pedestrian safety at intersections. Nonetheless, UDOT has encountered varying research findings regarding LPIs, with some studies highlighting potential safety concerns. As a result, UDOT remains cautious about widespread LPI implementation. According to the Federal Highway Administration (FHWA), theoretical benefits of LPIs include improved visibility for crossing pedestrians, fewer conflicts between vehicles and pedestrians, and increased motorist yielding. However, potential drawbacks include the possibility of increased pedestrian-vehicle interactions, necessary restrictions on right turn on red (RTOR), reduced green time for vehicles, decreased operational efficiency, and greater complexity in signal timing. The primary concern is the risk of conflicts between right-turning vehicles and pedestrians, particularly regarding the need for "No Right Turn on Red" (NRTOR) restrictions when LPIs are implemented. UDOT is also interested in evaluating compliance with NRTOR controls.
The purpose of this research is to assess LPI installations within Utah through a before-and-after analysis. The outcomes will inform the development of formal departmental guidance for future LPI implementations, addressing the current absence of documented procedures due to inconclusive evidence on the efficacy of LPIs.
]]></description>
      <pubDate>Mon, 13 Oct 2025 17:38:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2608462</guid>
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      <title>Impact of Turning Radius on Vehicle Speeds and Pedestrian Crossing Safety on Urban Unsignalized Intersections Using Virtual Reality</title>
      <link>https://rip.trb.org/View/1853635</link>
      <description><![CDATA[The study of road user behavior at intersections and its impact on pedestrian safety has significant relevance. About 62% of the fatal intersection crashes involving pedestrians happened at unsignalized intersections. Roudsari et al. (2007) found that 67% of pedestrians involved in right-turn collisions were hit from their left side and that the difference in impact speed is a significant predictor of severe injuries and fatalities. Crashes between right-turning vehicles, pedestrians, and bicyclists are common at intersections. The severity of these crashes is primarily a function of the speed of the turning vehicle, which can be influenced by the curb radius, the presence of exclusive or channelized turning lanes, and the type of intersection traffic control. TRB (2020) indicates there are no extensive studies on the effect of reducing curb radii on intersection-related crashes involving pedestrians. 
The objective of this research is to develop a Virtual Reality (VR) simulation study that can evaluate the impact that different curb radii values have on driver and pedestrian behavior in unsignalized intersections. This study will use the HTC Vive Pro Eye VR headset to measure the behavior of drivers and pedestrians on right-turning and crossing maneuvers, respectively, on an urban unsignalized intersection. Drivers will analyze traffic and roadway conditions to select their speeds when making a right-turn at an intersection and react to pedestrian crossing maneuvers on the side street. Pedestrians will analyze the existing roadway and traffic conditions at the intersection to decide when an acceptable safe vehicle gap is available in the right-turning and side street traffic flows to cross at the corner of an intersection. The study will analyze the driver’s speed and yield rate when approaching crosswalks from the right-turn maneuver, the pedestrian’s ability to detect safe vehicle gap times, crossing speeds and success rates, and road user’s behavior when responding to unexpected hazardous situations.]]></description>
      <pubDate>Mon, 24 May 2021 11:39:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/1853635</guid>
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    <item>
      <title> Creating a Situation-aware Sensing Environment for Cyclists: An Innovative and Cost-effective Smartphone-based Approach</title>
      <link>https://rip.trb.org/View/1756017</link>
      <description><![CDATA[This research will assess the feasibility and effectiveness of a Biker Assistance System (BAS) in different roadway contexts using a prototype mobile application. The application would make use of smartphones’ onboard speaker and microphones to monitor potential hazards and help bicyclists avoid crashes. The application will detect potential hazards by emitting an imperceptible sound and interpreting its reverberations, thus becoming a “mini-sonar system.” When certain potential hazards are detected, the smartphone will alert bicyclists of the hazard. This new approach to preventing bicycle crashes has yet to be developed or tested to the researchers’ knowledge. 
This project has four components. First, the project team proposes to analyze existing crash data sources to understand the types of crashes that can be prevented or mitigated with BAS. Second, the team proposes the development of the BAS for at least two hazardous scenarios – right turning vehicle detection and front/overtaking vehicle nearing. Additional scenarios may be added based on the crash data assessment. Third, a bike simulator study will be conducted to determine effective alerts for selected hazards. Based on the simulator study outcomes, a list of multi-modular alerts will be recommended which can be easily understood and interpreted by cyclists under both day and night lights. These alerts will be included in the BAS prototype. Finally, the project team proposes testing the efficacy of BAS in these scenarios via physical testing and naturalistic observation using an instrumented bicycle. This naturalistic database will be used to identify the critical cyclists-vehicle interaction regions and scenarios. Future research will expand the sensing capacity to function in different crash scenarios, investigate cyclists’ interactions with different road users, and provide cyclists with feedback to avoid different types of on-road hazards.
]]></description>
      <pubDate>Sat, 05 Dec 2020 18:16:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/1756017</guid>
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      <title>Right-turn Traffic Volume Adjustment in Traffic Signal Warrant Analysis</title>
      <link>https://rip.trb.org/View/1412297</link>
      <description><![CDATA[Right-turn traffic does not affect intersection performance in the same magnitude as through or left-turn traffic. Therefore, it is necessary to apply an adjustment to the right-turn volume when conducting signal warrant analysis. Without any reduction, an intersection with heavy right-turn volume might mislead the signal warrant analysis result, and could make a difference in whether a signal is deemed warranted or not. 
This research involved development of a guideline for determining the amount of reduction for right-turn traffic while performing signal warrant analysis. The proposed guideline was based on the delay equivalent relationship between right-turn and through traffic, i.e., the right-turn volume that is equivalent to a number of through vehicles, which would produce the same control delay on the minor street. The equivalent factor was defined as the measurement of the reduction of right turns. Because equivalent factors were calculated based on delay, it incorporated major impact factors of the right-turn and through traffic inherently, such as conflicting flow rates, capacity, critical headways, follow-up headways, and pedestrian crossing impact. Case studies were also conducted to demonstrate the applicability of the proposed guideline. 
]]></description>
      <pubDate>Tue, 21 Jun 2016 07:57:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1412297</guid>
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