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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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      <title>Observational Intersection Traffic Safety Analysis</title>
      <link>https://rip.trb.org/View/2655705</link>
      <description><![CDATA[While planners and engineers design intersections with safety in mind, the intended use and actual use of facilities do not always align. This misalignment can lead to increased safety risks for all intersection participants, particularly non-motorized users including pedestrians and cyclists. Although facility utilization mismatches can be detected through observation, typical monitoring occurs only during limited peak hours, failing to fully capture comprehensive usage patterns and emerging safety concerns.

This research proposes long-term intersection monitoring to uncover emerging facility utilization patterns and assess inherent intersection safety. The approach leverages existing traffic camera infrastructure combined with modern deep learning techniques for accurate detection and tracking of vehicles, bicycles, and pedestrians. As an explicit use case, the study examines unprotected left-turns to characterize both vehicle-vehicle conflicts through time gap analysis and trajectory conflicts involving other road users. The project develops a computer vision system capable of processing trajectories to quantify left-turns with insufficient gaps, instances where vehicles fail to yield appropriately, and average time gaps, collectively providing metrics to characterize intersection safety.

This interdisciplinary project combines computer vision algorithm development expertise from the University of Nevada, Las Vegas (UNLV) with programming support from Howard University. System evaluation will occur at intersections in both the Washington, DC area and the Las Vegas metropolitan area, utilizing purpose-built high-resolution monitoring equipment for short-term deployment as well as existing lower-resolution traffic cameras for long-term analysis. The project leverages intersection equipment acquired through NSF Award Number 2216489.

Expected outcomes include research contributions in computer vision and machine learning for trajectory analysis, workforce development through student training across both institutions, and technology transfer through publications on intersection safety scoring and practitioner engagement for field deployment.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:30:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655705</guid>
    </item>
    <item>
      <title>Evaluating Safety Impacts of Permissive Green Signal Phasing on Observed Conflicts Between Left-Turning Vehicles and Pedestrians</title>
      <link>https://rip.trb.org/View/2625837</link>
      <description><![CDATA[Permissive left-turn signal phasing allows vehicles to make left turns while pedestrians may concurrently receive a “walk” signal on parallel crosswalks. These simultaneous movements create elevated risks for pedestrians, as drivers often focus solely on identifying safe gaps in opposing through traffic. Currently, there are no clear recommendations directing state departments of transportation (DOTs) regarding permissive left turns and pedestrians. As current Nevada Department of Transportation (NDOT) practices lack evidence-based, clear, context-sensitive treatments that can translate into consistent pedestrian safety improvement, an integrated study is essential to mitigate pedestrian safety risks.]]></description>
      <pubDate>Mon, 17 Nov 2025 18:34:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625837</guid>
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    <item>
      <title>Sequencing for Phases with Flashing Yellow Arrow</title>
      <link>https://rip.trb.org/View/2608463</link>
      <description><![CDATA[When the Utah Department of Transportation (UDOT) first implemented flashing yellow arrows (FYA) for left-turn phasing, high crash rates were observed with lagging FYA operations. As a result, current UDOT policy for FYA operations for protected/permitted left turn phasing is to not “lag,” meaning to allow the protected left turn phase to follow the opposing through movement, due to a “perceived yellow trap.”

The purpose of this research is to evaluate current UDOT policies on leading/lagging left turn sequencing to determine if more flexibility could be provided, while still maintaining an acceptable level of safety. This will include reviewing the operation of FYA signal heads in other states, along with reviewing potential driver behaviors leading to the “perceived yellow trap.” The research will compare UDOT policies with other state DOT policies on leading/lagging left turns with FYA. Design and hardware factors will also be analyzed for safety impacts.
]]></description>
      <pubDate>Mon, 13 Oct 2025 18:57:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2608463</guid>
    </item>
    <item>
      <title>Improve Safety of Vehicles and Vulnerable Road Users at Intersections Integrating C-V2X and LiDAR Sensing Technologies</title>
      <link>https://rip.trb.org/View/2447164</link>
      <description><![CDATA[Crash risks associated with permissive left turns (PLTs), vulnerable road users (VRUs), and wrong way driving (WWD) appear to be among the top attributing factors for fatal and incapacitating crashes at signalized intersections, raising alarming safety concerns. The cellular vehicle-to-everything (C-V2X) communication and innovative traffic detection technologies such as light detection and ranging (LiDAR) sensing have the potential to reduce crashes related to these factors and enhance intersection safety. The research team will develop, test, and demonstrate a prototype system integrating C-V2X communication and innovative traffic detection technologies to improve safety of all road users at signalized intersections. Another objective is to provide the Texas Department of Transportation with guidelines for statewide implementation of the integrated system for intersection safety improvement. The research team will: (1) Review the literature on safety improvement systems utilizing C-V2X and innovative traffic detection systems; (2) Assess user needs and design a system architecture that satisfies user requirements; (3) Develop, test, demonstrate, and evaluate a prototype system that integrates C-V2X communication, LiDAR and other advanced traffic detection technologies to detect and mitigate crash risks involving PLTs, VRUs, and WWD at signalized intersections; and (4) Develop guideline documents containing implementation procedures and use case scenarios.]]></description>
      <pubDate>Wed, 30 Oct 2024 15:29:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447164</guid>
    </item>
    <item>
      <title>Integrating Progression Band and Delay Optimization for Arterials with Unbalanced Directional Traffic</title>
      <link>https://rip.trb.org/View/2343710</link>
      <description><![CDATA[Unbalanced directional traffic is commonly observed in commuting corridors, where the high-volume direction may experience queue spillbacks and turning bay blockages that significantly downgrade the traffic efficiency. The traditional wisdom that naturally favors the high-volume direction often neglect the needs of the low-volume direction, incurring unnecessary delays. Such a dilemma raises a challenging need for a signal plan that concurrently ensures the traffic efficiency of both directions with distinct traffic features.
Fully recognizing the achievement of two major families of signal optimization models, delay minimization and progression maximization, this project intend to integrate their merits, and present a novel traffic signal model to minimize the through delay in the high-volume direction while preserving the progression in the low-volume direction. To achieve such an objective, this project will develop a mathematical programming framework with essential formulations. Especially, to estimate the queueing delay accurately with signal related parameters, unlike most existing studies assuming the uniformly distributed incoming traffic flow, the project will explicitly formulate the queue evolution process by accounting for the time-varying vehicle arrival rates resulting from the distinct upstream traffic streams. Such a detailed formulation shall enable the model to flexibly select the optimal phase sequences that allow low-volume traffic streams to
join the queue prior to those high-volume ones, thus minimizing queuing delays despite the maximum queue length being inevitably long. Moreover, the negative impacts of left turn vehicles merging into through queues due to the left-turn bay blockage by the expanding through queue should be also taken into consideration in the through delay computation. The proposed model with carefully designed formulations is expected to yield improved network-wide delay, fewer vehicle stops, and a shorter duration of left-turn bay blockage.]]></description>
      <pubDate>Thu, 22 Feb 2024 15:54:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343710</guid>
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    <item>
      <title>Improving Crash Prediction Using GRIDSMART Infrastructure</title>
      <link>https://rip.trb.org/View/2325689</link>
      <description><![CDATA["Improving Crash Prediction Using GRIDSMART Infrastructure" is a collaborative research project between the University of Connecticut and the University of Maine, in partnership with the Connecticut Department of Transportation (CTDOT). The study addresses the critical issue of intersection safety, noting that over 50% of all fatal and injury crashes occur at or near intersections, with left turn maneuvers accounting for a significant proportion of these crashes. The research aims to enhance Safety Performance Functions (SPFs) by integrating temporally varying data, such as continuous turning movement counts, into their development. This approach overcomes the limitations of traditional SPFs that mainly consider static measures like the two-way annual average daily traffic (AADT).

The project will utilize GRIDSMART technology, a system implemented for traffic signal management, to collect detailed turning and through movements of various road users at intersections. This data will be used to develop more precise and dynamic SPFs, considering factors like bicycle and pedestrian counts, road geometry, and surrounding land development intensity. The dependent variables in the study will include annual crash counts by manner of collision, categorized by the direction of travel of the involved vehicles.

The methodology involves collecting raw data using GRIDSMART, identifying relevant variables, and selecting intersections based on data quality for analysis. The team will perform temporal aggregation of data and classify crashes by manner of collision. The resulting analysis aims to determine whether turning movements are significant predictors of crashes and how improvements to GRIDSMART infrastructure could enhance intersection safety.]]></description>
      <pubDate>Mon, 22 Jan 2024 09:44:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325689</guid>
    </item>
    <item>
      <title>Field-Based Evaluation of Left Turn Behavior Variations at the Individual Driver/Vehicle Level</title>
      <link>https://rip.trb.org/View/1853639</link>
      <description><![CDATA[Left turns are a complicated maneuver at signalized intersections that to accurately model using microscopic (agent-based) simulation requires calibration process that are typically focused on making changes to simulation parameters until field conditions and simulation conditions are comparable. Simulation parameters are often based on hard cut-off points and the definition of zones that, when adjusted, result in a desired driver behavior. This means that, unlike traditional speed input parameters options, defining a distribution for parameters like the critical gap is not an option in common microscopic simulation tools even when gap acceptance behavior is known to be impacted by waiting time, position in the queue, and arguably is likely to be impacted by factors beyond what controlled experiments reveal. Therefore, it is arguably beneficial to incorporate a variation in left turn gap acceptance behavior in agent-based simulations to accurately represent field conditions. This project will contribute to the collective understanding of left turn behavior by monitoring the gap acceptance behavior of individual drivers over time and evaluating the impact that such knowledge can have on the accuracy of agent-based simulations, especially when a probabilistic model that governs gap acceptance are introduced.]]></description>
      <pubDate>Mon, 24 May 2021 11:28:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/1853639</guid>
    </item>
    <item>
      <title>Effect of Large Vehicles on Left Turn Gap Acceptance at Signalized Intersections</title>
      <link>https://rip.trb.org/View/1705307</link>
      <description><![CDATA[Driver gap acceptance behavior is a function of geometric and operational factors. An essential measure of gap acceptance is the critical gap. At signalized intersections with permissive left turn movements, the critical gap is the foundation to estimate left turn capacity. Several studies have identified geometric, temporal, spatial, and operational effects on the critical gap estimates. However, recommended critical gap values have remained unchanged for decades. The Highway Capacity Manual recommended critical gap for left turns at signalized intersections is 4.5 seconds and the follow-up headway is 2.5 seconds. Most conventional signal optimization software (PASSER, SHYCHRO, VISSIM, SIDRA, HCS) use these outdated critical gap and follow-up headway estimates without accounting for specific traffic conditions such as large vehicles affecting gap acceptance. As part of this study, observational data and gap acceptance estimates with the presence of large vehicles will be integrated with microsimulation and signal optimization software to evaluate the effect of large vehicles in operational performance of left turning vehicles at signalized intersections.]]></description>
      <pubDate>Thu, 07 May 2020 12:53:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1705307</guid>
    </item>
    <item>
      <title>Reasonable Alternatives for Grade-Separated Intersection</title>
      <link>https://rip.trb.org/View/1472678</link>
      <description><![CDATA[Grade-separated intersections increase the capacity of two non-freeway roads by elevating two or more approaches, thereby removing conflict points. Despite not including a freeway, most of the grade-separated intersections are designed using freeway-level concepts such as loop ramps. While sometimes efficient, this results in excessive right-of-way needs, increased instances of pedestrians crossing free-flow movements, and over design.
This project will develop renderings of each of the left turning options including general guidance on median placement, lane assignments, and right-of-way needs. General determination of cost as well as vehicular and pedestrian safety impacts will be detailed in the guidance for planning and design professionals. Because some innovative intersections have been patented, a patent landscape analysis will investigate the existing patents in grade-separated intersection designs. Finally, both deterministic and stochastic operational analysis will be conducted, with a micro-simulation analysis for the designs with the greatest potential for implementation in North Carolina. With this guidance on cost, safety, and operations, North Carolina Department of Transportation (NCDOT) planners and engineers will be able to more appropriately select a grade-separated intersection design for various vehicular and pedestrian volume levels. 
]]></description>
      <pubDate>Fri, 30 Jun 2017 15:37:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1472678</guid>
    </item>
    <item>
      <title>Evaluation of Diverging Diamond Interchanges for Alabama, Phase I</title>
      <link>https://rip.trb.org/View/1260467</link>
      <description><![CDATA[The goal of this research is to develop guidance for the Alabama Department of Transportation (ALDOT) to establish the locations where a diverging diamond interchange (DDI) would be beneficial.  The University of Alabama (UA) will assist the University of Alabama - Huntsville (UAH) to perform the proposed research.  The objective of this project is to assist on tasks associated with DDI project funded by ALDOT.  This will require the subcontractor to run simulations and evaluate alternatives. The DDI, or Double Crossover Diamond (DCD), is a form of interchange design upon which the intersections of the cross-over road and the entrance/exit ramps are constructed to remove all left turns.  The benefits of the interchange design stem from the removal of left-turns at the entrance/exit ramp signals, thus reducing the number of phases required improving traffic flow and reducing the number of conflict points improving safety.]]></description>
      <pubDate>Fri, 30 Aug 2013 01:01:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1260467</guid>
    </item>
    <item>
      <title>Improving Transportation Performance: The Case of Left Turns</title>
      <link>https://rip.trb.org/View/1239204</link>
      <description><![CDATA[Over the past century, the automobile has evolved to dominate transportation not only from a behavioral perspective but from an infrastructure perspective. Thoroughfares that evolved over millennia to serve many users were transformed in decades to the near exclusive use by motor vehicles. The reasons for this evolution are well documented; alternatives to the behavioral dominance, while numerous in terms of proposals and promise, are nevertheless constrained by the infrastructural dominance. One option that has not been systematically studied but that has the cost advantage of maintaining current infrastructure while addressing associated performance impacts is a significant reduction in allowed arterial left turns. Such a policy will soon become feasible with the rapid adoption of global positioning system (GPS) and traveler information systems that can inform drivers of optimal route choice in restricted networks. The proposed research will use a microsimulation approach to investigate a range of left turn restriction and removal options on sample arterial networks, under a range of driver behavior assumptions.]]></description>
      <pubDate>Thu, 31 Jan 2013 01:01:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/1239204</guid>
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