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    <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" />
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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>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Roadmap for Virtual Traffic Light Control at Intersections Using Connectivity, AI and Digital Twins</title>
      <link>https://rip.trb.org/View/2786712</link>
      <description><![CDATA[Vehicle automation, connectivity, artificial intelligence (AI), and advanced sensing enable new approaches to intersection management. This project investigates a virtual traffic light (VTL) framework that over time can replace conventional signalized intersections, which carry high capital, maintenance, and operating costs. In the proposed system, connected and automated vehicles (CAVs), along with connected pedestrians and bicyclists, communicate continuously with a virtual control agent that maintains real-time estimates of their positions, speeds, and intended trajectories. Using these data, the agent computes individualized right-of-way assignments—equivalent to virtual green, yellow, and red indications—and transmits them directly via vehicle-to-everything (V2X) communication to each user. By coordinating movements through communication rather than fixed infrastructure, the system can improve traffic efficiency, reduce delays, enhance safety, and lower costs. An additional advantage of VTL systems is their flexibility: control logic and policies can be updated through software, enabling rapid, low-cost adaptation compared with conventional traffic signals. Because full connectivity will be achieved gradually, the research first establishes upper-bound performance under ideal, fully connected conditions without failures, providing quantitative benchmarks. It then develops a roadmap for transitioning from today’s traffic control to increasingly connected environments, accommodating mixed traffic with varying levels of connectivity. The results will guide phased deployment strategies aligned with USDOT priorities in safety, mobility, sustainability, and cost-effective infrastructure modernization.

]]></description>
      <pubDate>Mon, 05 Oct 2026 13:32:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786712</guid>
    </item>
    <item>
      <title> 
Impacts of Left-Turn Traffic Signal Phasing Strategies on Safety Outcomes
</title>
      <link>https://rip.trb.org/View/2762048</link>
      <description><![CDATA[Left-turn (LT) maneuvers are among the most challenging and highest-risk movements at signalized intersections. Transportation agencies across the United States have adopted varied signal phasing and operational strategies to manage LT movements, such as Flashing Yellow Arrow (FYA) displays in lieu of the Solid Green Ball for permissive turns, and FYA used in place of or in combination with protected-only phasing. While prior studies have examined safety performance at regional or whole-intersection levels, limited information exists at the intersection-approach level, where phasing decisions are actually implemented.
This project develops a methodology for approach-level safety analyses at signalized intersections based on left-turn signal phasing and operational strategies, focusing on crash involvement and outcomes in the vicinity of signalized intersections. The methodology quantifies and compares safety outcomes across locations, time periods, and operational strategies (e.g., before-and-after conditions; peak versus off-peak periods) using crash frequencies, severity outcomes, and exposure-adjusted rates, with analytical techniques including non-parametric statistical tests. Application is demonstrated through a Las Vegas metropolitan area case study, drawing on crash records from Nevada Department of Transportation (NDOT) and local agencies; signal, roadway, and traffic characteristics from the RTC of Southern Nevada; and vehicle trajectory and operational data (e.g., Streetlight, INRIX) from regional partners.
Results will advance the state of the science and practice for approach-level LT safety analysis and provide transportation agencies a basis to refine policies and procedures that enhance safety and operational efficiency at signalized intersections.
]]></description>
      <pubDate>Wed, 19 Aug 2026 16:02:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762048</guid>
    </item>
    <item>
      <title>Observational Intersection Traffic Safety Analysis II: Vision-Based Surrogate Safety Analysis</title>
      <link>https://rip.trb.org/View/2762043</link>
      <description><![CDATA[Intersections are among the most safety-critical locations in roadway networks, yet detailed data to support proactive intersection safety analysis remain limited beyond the basic detection used for signal controller operations. Building on Phase I advances in efficient vehicle detection and tracking, this project leverages existing camera-based detection deployments, including those in Clark County, Nevada, to move vision-based surrogate safety analysis toward technology transfer and operational deployment.
The project uses road-user trajectories produced by Phase I models to compute surrogate safety indicators, principally time-to-collision (TTC) and post-encroachment time (PET), and develops a comprehensive intersection safety score that combines surrogate measure distributions with exposure. Key tasks include obtaining access to live intersection video feeds; implementing trajectory-based safety analysis code; developing the composite safety score; deploying cloud-based, GPU-enabled computing infrastructure with backend database storage for live video analysis; and building a web-based front-end platform presenting maps, safety scores, heat maps, and detailed analytics.
Anticipated outcomes include computer vision algorithms for detecting and tracking all road users; trajectory-based indicators covering turning movement counts, pedestrian and bicyclist activity, TTC, and PET; and an operational web platform for storing, visualizing, and reporting safety measurements. Open-source code and datasets will be released publicly, and the team will coordinate with the Regional Transportation Commission of Southern Nevada to ensure tool utility, enabling agencies to identify safety risks earlier and target interventions proactively.
]]></description>
      <pubDate>Wed, 19 Aug 2026 11:52:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762043</guid>
    </item>
    <item>
      <title>Development of a CAV Testbed-enhanced Smart Campus at Morgan State University – Phase IV: Smart Corridor Expansion and Data-Driven Intersection Safety Monitoring</title>
      <link>https://rip.trb.org/View/2742148</link>
      <description><![CDATA[Phase IV builds upon the infrastructure and algorithms developed in Phases I through III by expanding the SMART corridor testbed and introducing a data-driven framework for continuous intersection safety monitoring. While earlier phases focused on LiDAR-enabled signal control and connected vehicle messaging, this phase emphasizes corridor expansion, safety analytics, and statewide V2X readiness.

The project analyzes pedestrian safety near the pedestrian bridge within the SMART corridor to understand unsafe crossing behavior and vehicle-pedestrian conflicts, and develops a new Intersection Safety Score framework using LiDAR trajectory data and signal-violation metrics, integrated into a Power BI dashboard for real-time monitoring. The SMART corridor testbed will be expanded to the Hillen Road and Harford Road intersection, a statewide inventory and operational assessment of Roadside Units (RSUs) across Maryland will be conducted, and the team will collaborate with Baltimore City DOT to integrate advanced V2X signal applications including SPaT broadcasting and LiDAR-based signal actuation.

The project will deliver a comprehensive analysis of pedestrian safety risks near the SMART corridor pedestrian bridge; an Intersection Safety Score methodology that quantifies intersection risk using trajectory-based conflict analysis of red- and yellow-light violations, vehicle-vehicle and vehicle-pedestrian conflicts, and near-miss events; and a Power BI safety-monitoring dashboard enabling agencies to visualize and track intersection safety performance. It will expand the SMART corridor testbed to the Hillen Road and Harford Road intersection through LiDAR installation, sensor calibration, and integration with existing connected vehicle systems. A statewide inventory and operational readiness assessment of Maryland’s RSUs will evaluate device status, firmware compatibility, and SAE J2735 messaging capability, and the project will conduct field validation of V2X-enabled signal applications, including SPaT broadcasting, LiDAR-based signal actuation, and trajectory-based interventions such as dynamic all-red extensions.

]]></description>
      <pubDate>Sat, 01 Aug 2026 10:38:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742148</guid>
    </item>
    <item>
      <title>Enhancing Traffic Signal Controller Performance using LiDAR data.</title>
      <link>https://rip.trb.org/View/2742142</link>
      <description><![CDATA[Enhancing Traffic Signal Controller Performance Using LiDAR Data applies software-in-the-loop testing to a previously developed approach for optimizing traffic signal timings to reduce vehicle delays and increase throughput at signalized intersections, serving as a first step prior to field deployment. Field data will be gathered at a single signalized intersection using LiDAR equipment to investigate the potential of LiDAR data for the field evaluation of traffic signal controllers.

Using the field data, optimized signal timings will be computed and implemented in a simulation environment along a segment of a major arterial. Conducted in collaboration with the Virginia Department of Transportation (VDOT), the study builds on the team’s prior work demonstrating that traditional methods such as the Webster formulation overestimate cycle lengths and fail to minimize delay under congested conditions, and applies a new Laguna-Du-Rakha (LDR) formulation that minimizes delay while reducing stops, deceleration, and acceleration at signalized intersections.

The project will collect LiDAR field data at the signalized intersection of Cloverdale Road and Lee Highway to evaluate the suitability of these data for the field evaluation of signal control strategies. The team will compute optimum cycle lengths using the LDR formulae, extract vehicle trajectory data for the computation of queue lengths, delays, stops, and fuel and energy consumption, and construct and calibrate a simulation network of the intersection. Four signal timing plans, fixed-time and actuated control based on both the Webster and LDR methods, will be implemented and evaluated across ten traffic demand levels, with emphasis on high-demand conditions where the LDR formulation provides the greatest benefit. Pedestrian data will also be collected and incorporated.]]></description>
      <pubDate>Sat, 01 Aug 2026 09:50:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742142</guid>
    </item>
    <item>
      <title>Portable Tool for Periodic Evaluations of Intersection Signal Timings</title>
      <link>https://rip.trb.org/View/2725460</link>
      <description><![CDATA[The safety and efficiency of traffic-signal-controlled intersections rely on having appropriate timing signals for intersection users. Yellow, all-red, and crosswalk timings that are too low for users create the potential for intersection conflicts. Similarly, effective green times that are too small for volumes create long queues and congestion. However, signal timings are typically only evaluated every 2-5 years when signals are retimed through an often time-consuming process. The proposed solution is a portable device containing low-cost off-the-shelf radar and camera sensors that will record the trajectories of intersection users differentiating between pedestrians, bicycles and vehicles.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:36:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725460</guid>
    </item>
    <item>
      <title>Developing Guidelines for Right-Turn Lane Pockets for Intersections in Nevada</title>
      <link>https://rip.trb.org/View/2713605</link>
      <description><![CDATA[Nevada is overrepresented in intersection-related crashes and has been designated as an Intersection-Focused state by the Federal Highway Administration (FHWA).  Further research exploring how dedicated right-turn lane pockets could mitigate fatal and serious injury crashes is needed.  Currently, the criteria used to determine when an exclusive right-turn lane is required may be inconsistently applied given today’s traffic volumes, vehicle mix, pedestrian activity, and safety expectations. In some cases, right-turn lanes are omitted even when they could improve operations or safety, while in others, they are added despite limited benefits and significant cost or right-of-way impacts.
The main objective of this research is to produce an updated, data-driven, and context-sensitive framework for determining when and where exclusive right-turn lane pockets should be required or clearly not required at access points in Nevada.  In particular, a tool will be provided that will improve the Nevada Department of Transportation’s (NDOT’s) ability to consistently evaluate access points to the state highway system and will maximize the safety and operational benefits of exclusive right-turn lanes. Additionally, the research results will provide NDOT with a process model that supports consistent, transparent, and technically-sound decision making for new developments and access modifications.
The University of Nevada, Reno team plans to reach the research objective by: (1) Synthesizing the current state of knowledge and practice for right-turn lane warrants nationally and among peer agencies. (2) Collecting high-resolution field data and developing calibrated VISSIM microsimulation models.  (3) Conducting systematic scenario-based simulations using the calibrated VISSIM models and crash data analyses.  (4) Developing guidelines, using the empirical findings from the aforementioned tasks, for determining when an exclusive right-turn lane pocket is warranted. (5) Developing a user-friendly, implementation-ready decision-support tool for use by NDOT reviewers, local agency staff, developers, and consulting engineers. (6) Compiling all research findings, guidelines, and tools into a comprehensive final report and conducting a training workshop to facilitate implementation.
The final deliverables will be designed from the outset for direct integration into NDOT’s policies, procedures, and operations. 
Additionally, the implementation plan will be structured as a phased approach that transitions from research completion through pilot application, full deployment, and sustained use.]]></description>
      <pubDate>Thu, 11 Jun 2026 14:33:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2713605</guid>
    </item>
    <item>
      <title>Guidance for Selecting Pedestrian Safety Treatments at Signalized Intersections to Address Permitted Turn Conflicts</title>
      <link>https://rip.trb.org/View/2712202</link>
      <description><![CDATA[State departments of transportation (DOTs) and local agencies work to improve pedestrian safety at signalized intersections while maintaining efficient vehicle operations. Traditional signal timing practices often allow pedestrians to cross concurrently with permitted turning vehicles, which can create unsafe or stressful conditions for pedestrians, particularly at intersections with high turning volumes and speed or complex geometries. Agencies have implemented treatments such as leading pedestrian intervals (LPIs), delayed-turn strategies, and protected-only turn phases, but these applications are often applied without nationally consistent, data-driven guidance.

Existing resources identify available pedestrian safety treatments but provide limited guidance on when specific strategies are most appropriate. This can lead to inconsistent practices and difficulty balancing pedestrian safety improvements with operational impacts to vehicles.

The objective of this research is to develop a data-driven guide for selecting pedestrian safety treatments at signalized intersections to address conflicts with permitted turning vehicles. Using field-collected data, the research will evaluate treatments under varying traffic, geometric, and signal timing conditions, with results to help agencies identify appropriate strategies.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:11:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712202</guid>
    </item>
    <item>
      <title>Use of Bicycle Signal Faces in Applications with Concurrent Turning Traffic</title>
      <link>https://rip.trb.org/View/2709246</link>
      <description><![CDATA[Bicycle infrastructure is expanding rapidly across the United States. Conflict involving bicyclists and other users at urban intersections is a growing safety concern. Various signalization strategies have been employed to govern bicycle and turning-vehicle movements, but the use of the bicycle signals has been restricted to locations where bicycle movement is phase-separated from turning traffic.

Because dedicated bicycle phases can substantially reduce green time for both cyclists and turning vehicles, the added delay may reduce red-signal compliance or encourage cyclists to use less-protected routes or ride in mixed traffic.

Research is needed to better understand the safety and operational effects of bicycle signals. This includes evaluating tradeoffs for cyclists and drivers when phase separation is required; documenting current methods that allow cyclists to proceed concurrently with turning vehicles, with or without bicycle signal indications; and comparing compliance and crash outcomes associated with different signalization strategies.

OBJECTIVE: The objective of this research is to explore the feasibility and implications of configuring bicycle signals to permit bicycles to move concurrently with turning traffic.]]></description>
      <pubDate>Tue, 02 Jun 2026 14:44:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709246</guid>
    </item>
    <item>
      <title>Real-Time Anomaly Detection System for Signalized Intersections</title>
      <link>https://rip.trb.org/View/2673038</link>
      <description><![CDATA[Automated Traffic Signal Performance Metrices (ATSPMs) rely on high-resolution controller data to support signal operations and maintenance. While existing ATSPM Watchdog tools can identify certain detector malfunctions, they typically rely on static thresholds set across the entire system, limiting their ability to detect issues promptly and adapt to site-specific traffic patterns. This project develops and evaluates a statistical, spatiotemporal anomaly detection framework that leverages historical detector behavior and agreement among neighboring detectors to improve alert timeliness and accuracy. The research will characterize practitioner needs, define normal detector behavior, develop and test statistical detection and classification methods, and quantify benefits relative to static thresholds. Results will inform a roadmap for potential real-time implementation within Virginia Department of Transportation (DOT) systems.]]></description>
      <pubDate>Tue, 24 Feb 2026 10:25:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2673038</guid>
    </item>
    <item>
      <title>Crash Analysis at Intersections with Transit Signal Priority</title>
      <link>https://rip.trb.org/View/2643022</link>
      <description><![CDATA[Transit Signal Priority (TSP) systems are widely used to improve transit operations at signalized intersections, but variations in signal display design may affect how drivers interpret and respond to these signals. Although recent updates to the Manual on Uniform Traffic Control Devices  (MUTCD) revised standards for certain TSP indications, many jurisdictions continue to operate legacy signal designs. The safety implications of these design differences are not well understood.

This project investigates the relationship between TSP signal display configurations and crash outcomes at intersections. The research will develop a national inventory of TSP implementation sites and associated signal design characteristics, compile crash histories for these locations, and analyze crash frequency and severity using statistical modeling. Crash report narratives will also be reviewed to identify behavioral factors linked to specific signal displays. Results will provide evidence-based guidance on transit signal design and support future updates to national standards, improving intersection safety while maintaining effective transit operations.]]></description>
      <pubDate>Thu, 18 Dec 2025 14:02:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643022</guid>
    </item>
    <item>
      <title>Develop and Test Optimal Speed Control Strategies for Connected and Automated Vehicles under GPS Jamming and Spoofing</title>
      <link>https://rip.trb.org/View/2640714</link>
      <description><![CDATA[This research project engages in transformative innovation to develop and evaluate optimal speed control strategies for Connected and Automated Vehicles (CAVs) navigating signalized intersections under conditions of Global Positioning System (GPS) jamming and spoofing. While recent studies have primarily focused on robust detection and mitigation techniques to safeguard CAV navigation, this study represents a first attempt to investigate the impact of GPS jamming and spoofing on CAV speed control applications and develop solutions to address these cyber attacks. The algorithms developed in this study also will be beneficial for extending into other CAV applications such as routing and platooning strategies. Building upon the Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I) framework, the study will generate real-time, fuel-efficient trajectories and enhance traffic flow efficiency within designated control zones. To ensure resilience against cyberattacks targeting GNSS signals, the project will simulate GPS spoofing and jamming scenarios in MATLAB and subsequently scale the evaluation to network-level performance using the INTEGRATION platform. Detection methods will leverage signal anomalies, estimation residuals, and cooperative vehicle-to-everything (V2X) cross-checks to identify compromised positioning data. The study further introduces a novel mitigation strategy using Optical Intelligent Reflecting Surfaces (OIRS) to enable dual-channel communication via RF and visible light. These OIRS-enabled systems will deliver authenticated positioning and timing data from roadside units, allowing the Eco-CACC-I controller to gracefully degrade and reweight sensor inputs when GNSS integrity is compromised. The outcome will be a robust, simulation-ready control strategy that enhances safety, fuel efficiency, and cyber-resilience for future CAV deployments. 

OBJECTIVES: The primary objective of this research is to develop and validate optimal speed control strategies for CAVs operating under compromised positioning conditions caused by GPS jamming and spoofing. By advancing the Eco-CACC-I framework, the study will produce fuel-efficient speed profiles for multiple vehicle powertrains, addressing both deceleration and uninterrupted cruising scenarios. These strategies will be tested in MATLAB and scaled to network-level simulations in INTEGRATION, aligning with USDOT priorities to promote safety, reduce congestion, and improve mobility and infrastructure durability. This study directly supports the statutory mission of the CARNATIONS UTC to toughen, augment, and protect Positioning, Navigation, and Timing (PNT) systems for multimodal surface transportation. ]]></description>
      <pubDate>Tue, 16 Dec 2025 15:20:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640714</guid>
    </item>
    <item>
      <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>
    </item>
    <item>
      <title>Developing Guidance for Calculating the Downstream Functional Area of an Intersection</title>
      <link>https://rip.trb.org/View/2628203</link>
      <description><![CDATA[Appendix F of the Virginia Department of Transportation (VDOT)’s Road Design Manual (RDM) provides guidance and standards for several spacing standards including entrance, corner clearance, and intersection functional area.  Of these, the spacing standard with the least guidance is on the dimensions of the functional area.  VDOT RDM has guidance derived from the American Association of State Highway and Transportation Officials (AASHTO) (2018) for upstream functional area dimensions but provides no guidance for the dimensions of the downstream functional area. The purpose of this project is to provide clearer guidance on calculating the downstream functional area for signalized intersections.  

The project tasks will include conducting a (1) literature review, (2) survey of best practices, and (3) exploratory crash analysis.  Based on the results of these tasks, guidance will be developed for inclusion into Appendix F of VDOT’s Road Design Manual (VDOT, 2025). This guidance will include methods and a spreadsheet-based tool for calculating appropriate areas (distances) for downstream functional areas as well as more descriptive details on calculating upstream functional areas.  The deliverable will be a final report documenting findings from the research tasks.  With clear appropriate guidance on functional area dimensions, more consistent entrance connection standards can be applied across the Commonwealth - thus improving predictability and saving time for VDOT, localities, and land developers in the approval process.   
]]></description>
      <pubDate>Thu, 20 Nov 2025 08:48:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2628203</guid>
    </item>
    <item>
      <title>Pedestrian Crashes at High-Speed Intersections: Applying AI for Crash Narrative Analysis to Identify Common and Edge Cases</title>
      <link>https://rip.trb.org/View/2625591</link>
      <description><![CDATA[High-speed intersections, with speed limits of 35 mph or higher, and long crossings are particularly risky for pedestrians. Tragically, in 2022, 983 pedestrians were killed at signalized intersections, representing about 16% of all pedestrian fatalities. Intersections are concerning due to pedestrians' difficulty navigating them and the numerous conflict points they present. The proposed research addresses a critical gap: understanding pedestrian crash descriptors using structured and unstructured (narrative) data on high-speed intersections and identifying simple and complex (or edge) cases—unusual or extreme crashes that deviate substantially from the typical ones. Complex cases represent exceptional circumstances with many contributing factors. Understanding them will help inform current pedestrian safety strategies and help improve how autonomous vehicle algorithms anticipate potential pedestrian conflicts.  The research question is: What are the different types of pedestrian crashes and injuries at intersections, and what are their complexity levels?  To answer this question, the team will use data from crashes from Tennessee’s Integrated Traffic Analysis Network (TITAN) and Wisconsin’s WisTransPortal, for which the research team has access to police crash narratives. The team will separate pedestrian crashes at high-speed signalized intersections (with speed limits of 35 mph or higher) and compare them to those at intersections with speed limits of less than 35 mph. The team will use AI to develop high-quality, detailed crash descriptors from the narratives of police reports and quantitative crash data. Natural language processing and feature extraction techniques will categorize pedestrian crashes into specific types based on detailed pre-crash actions, human errors, and circumstances obtained from structured and unstructured data. The study will identify edge cases and relevant safety countermeasures (e.g., conflict reductions) while providing a nuanced understanding of crash circumstances (relative to current practice).   The study will create a unique and comprehensive crash database that can provide deep insights into the range of injuries, crash attributes (e.g., crash location within the intersection or pedestrian and driver actions), precrash positions, driver and pedestrian impairment, and roadway conditions, and design (e.g., visibility, number of lanes, pedestrian crossing facilities). The study will apply rigorous analysis methods, including unsupervised learning techniques to identify complex cases and inference-based frequentist methods to quantify key correlates of crash injuries. Cluster analysis, specifically through hierarchical or k-means techniques, will differentiate complex crash cases from more common ones, effectively isolating extreme cases deviating from typical patterns.   In addition to highlighting the issue of pedestrian crashes at intersections and their correlates, a unique aspect of this study is the identification of complex cases. By doing so, the study aims to uncover the underlying patterns and risk factors that contribute to complex and unusual pedestrian crashes at intersections. Rather than focusing solely on the common crash situations, considering a wide range of possibilities and using a unique database helps to understand and address common and rare cases for high-speed and low/medium-speed intersections (especially relevant, given the adoption of vehicle automation and higher safety standards), ensuring a safer environment for vulnerable road users.]]></description>
      <pubDate>Mon, 17 Nov 2025 16:29:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625591</guid>
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