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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Enabling Next-Generation Safe, Efficient and Reliable Traffic Signal Management via Advanced Sensing and Foundation Models</title>
      <link>https://rip.trb.org/View/2691670</link>
      <description><![CDATA[Urban traffic signal management systems often rely on outdated techniques and strategies that fail to adapt to dynamic roadway conditions, leading to safety concerns, congestion, and access issues for road users. In addition, current signal optimization approaches rarely consider energy efficiency as the main objective. This research proposes a next-generation safe, efficient and reliable traffic signal control framework powered by advanced roadside sensing and foundation models, specifically Visual Language Models (VLMs) and Multi-Modal Large Language Models (MMLLMs). By integrating high-definition cameras, LiDAR, and real-time data analytics, the system will accurately detect multimodal traffic flows, predict future traffic conditions, and optimize signal phase and timings to enhance mobility while minimizing energy consumption. The framework will be validated through a case study at the Riverside Smart Intersection testbed, leveraging real-world data and co-simulation environments.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:42:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691670</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>LLM-Orchestrated Multi-Layer Digital Twin Network for Cyber-Resilient Traffic Management</title>
      <link>https://rip.trb.org/View/2663602</link>
      <description><![CDATA[Modern connected traffic systems are increasingly vulnerable to cyberattacks capable of propagating rapidly across networked infrastructure, inducing unsafe signal states, traffic congestion, and emergency response delays. Existing anomaly detection approaches including statistical thresholds, rule-based Automated Traffic Signal Performance Measures (ATSPM) and Signal Phase and Timing (SPaT) flags, and classical machine-learning methods such as Isolation Forest and one-class Support Vector Machines operate on limited data modalities and cannot capture cross-layer cyber-physical interactions or operator intent, leaving critical detection gaps in complex attack scenarios.
This project develops a distributed multi-layer digital twin (DT) network for urban traffic systems, enhanced by a large language model (LLM) for context-aware cyber anomaly detection. The framework mirrors physical traffic behavior, cyber infrastructure status, and operational decision processes across a corridor of 4–6 interconnected intersections, enabling early identification of unsafe and malicious events that threaten roadway safety. Each traffic unit is represented by coordinated Physical, Cyber, and Decision Layers: the Physical Layer models real-time mobility and safety conditions using ATSPM, SPaT/MAP data, and detector activity; the Cyber Layer mirrors controller firmware, communication telemetry, and roadside unit status; and the Decision Layer captures operator actions, timing plan updates, and agency-defined safety constraints. A customized transportation-aware LLM ingests both structured telemetry and unstructured logs to generate semantic feature embeddings that capture cross-layer and cross-node dependencies.
A hybrid neural anomaly detection engine integrates Temporal Convolutional Networks (TCNs) to learn evolving traffic and communication behaviors over time with Graph Neural Networks (GNNs) to capture spatial interactions and coordinated disruptions across interconnected intersections. This TCN–GNN architecture enables accurate recognition of both localized cyber intrusions and distributed corridor-level attacks. Detection performance is validated against controlled cyber-attack scenarios—including SPaT spoofing, firmware manipulation, and malicious timing-plan overrides—executed within the DT environment. Upon anomaly detection, the LLM generates actionable mitigation suggestions, such as isolating compromised controllers or reverting to safe fallback signal plans, which are evaluated within the digital twin to ensure that every recommendation supports operational safety, low latency, and service continuity.
The 12-month effort proceeds in two phases: development and calibration of the distributed multi-layer DTs with LLM integration for context modeling, followed by anomaly detection training, validation, and mitigation evaluation. Target performance metrics include detection accuracy of at least 90%, false-positive rates below 10%, decision-support latency improvements of at least 30%, and safety metric improvements of at least 20%. The project delivers a pilot-ready prototype, detailed deployment guidelines, and an open software repository to accelerate adoption by transportation agencies. 
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:28:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663602</guid>
    </item>
    <item>
      <title>Accelerating Deployment of MAP and SPaT Information Distribution Using Executable Designs
</title>
      <link>https://rip.trb.org/View/2625305</link>
      <description><![CDATA[The research team proposes MAP SPaT Digest and Digest Data Delivery functional objects using CubeDesigner to produce executable code that can be delivered to deployers who will run the code in edge physical objects. The evaluation criteria will be the successful operation of the functional objects in various host objects such as standalone Linux devices and cellular provider edge computing services. Also, the project will implement the Signal Control Status information flow using CubeProtocol so that the information integrity and communication efficiency claims of the protocol can be evaluated.]]></description>
      <pubDate>Thu, 13 Nov 2025 14:53:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625305</guid>
    </item>
    <item>
      <title>Develop a Data-Driven Intersection &amp; Interchange Control Evaluation (DIICE) Tool</title>
      <link>https://rip.trb.org/View/2614516</link>
      <description><![CDATA[Innovative intersection and interchange designs and smart technologies (e.g. adaptive traffic signal systems, connected vehicle technology, and real time traffic management) are often considered as methods to reduce congestion and promote safety. However, these designs are challenging and time-consuming to consider for a given project. The research team will develop a data-driven innovative design recommendation tool to provide guidance toward feasible design selections of innovative intersections and interchanges. The tool will be applicable in urban settings and consider factors like traffic volumes, safety enhancements, constructability, pedestrian and bicyclist accommodations, and costs. The tool will be complimented by guidance gathered from the literature and stakeholder interviews to help provide context and considerations that cannot be incorporated into the too. This will promote a consistent solution to aid in the intersection design selection process that can be used to reduce costs for considering innovative intersections and can be presented to policymakers as a data-driven recommendation. The research team will vet this prototype tool by applying the tools to existing innovative intersection/interchange design selections within Texas to demonstrate the effectiveness of the design selection solution.]]></description>
      <pubDate>Tue, 28 Oct 2025 11:20:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2614516</guid>
    </item>
    <item>
      <title>Field Deployment and Testing of Enhanced Fixed- and Actuated-Traffic Signal Control Systems</title>
      <link>https://rip.trb.org/View/2606409</link>
      <description><![CDATA[This research conducts field deployment and testing of enhanced traffic signal control systems using the Laguna-Du-Rakha formulation to optimize signal timings for reduced vehicle delays and fuel consumption at signalized intersections. Building on previous work demonstrating that traditional Webster formulation methods produce cycle lengths nearly three times longer than optimal under congested conditions, the study implements and validates improved signal timing approaches through real-world field testing in collaboration with Virginia Department of Transportation. The methodology involves identifying candidate intersections in the Blacksburg and Salem area, with primary focus on the Beamer Way and Southgate Drive intersection equipped with LiDAR surveillance instrumentation tracking objects within 150 meters. Optimized cycle lengths will be calculated using multi-objective optimization balancing delay minimization and fuel consumption reduction through adjustable weighting factors. Field implementation includes one-week deployment of optimized signal timing plans with LiDAR-based trajectory data collection for performance quantification including queue lengths, vehicle delays, stops, and fuel consumption measurements. VISSIM microsimulation modeling creates digital twins of selected intersections for validation against field data and sensitivity testing across various traffic demand levels and cycle length weight combinations, enabling assessment beyond observed field conditions and identification of optimal control strategies.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:18:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606409</guid>
    </item>
    <item>
      <title>Research Project Name: Development of a CAV Testbed-enhanced Smart Campus at Morgan State University - Phase III</title>
      <link>https://rip.trb.org/View/2606401</link>
      <description><![CDATA[This research advances Connected and Automated Vehicle (CAV) infrastructure through Phase III expansion of an established testbed, integrating LiDAR-powered safety applications with signal control systems and conducting comprehensive CAV market penetration analysis in partnership with Maryland Department of Transportation. Building on previous phases, the study coordinates signal phasing and timing across three campus intersections equipped with LiDAR and roadside unit infrastructure, implementing dynamic all-red extensions based on vehicle speed and red-light violation risk detection. The methodology develops pedestrian signal extensions activated by real-time crosswalk occupancy detection and creates Safety Data Sharing Messages compliant with SAE J2735 standards for broadcasting object-level data to vehicles. Portable LiDAR deployments collect trajectory data at additional intersections and work zones for solution validation. The market penetration analysis component catalogues CAV data sources, develops quality assurance frameworks, and compares traditional probe data with connected vehicle information. Collaboration with Maryland Motor Vehicle Administration provides vehicle registration cross-referencing with automation levels, while commercial vendor partnerships supply dynamic usage patterns. The research creates geographic information system (GIS)-based visualizations representing regional CAV penetration and develops interactive dashboards for transportation planning support.]]></description>
      <pubDate>Thu, 02 Oct 2025 14:53:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606401</guid>
    </item>
    <item>
      <title>Traffic Signal Non-Intrusive Detection Technology Assessment and Comparison</title>
      <link>https://rip.trb.org/View/2601431</link>
      <description><![CDATA[North Carolina Department of Transportation (NCDOT) has been using in-pavement (in-ground) magnetic induction loops to detect vehicles and operate traffic signals for decades as have many other state DOTs. Given the high number of signals in NC there are hundreds of thousands of loops that must be maintained for efficient signal operation and coordination. Many non-intrusive (above-ground or out-of-pavement) vehicle detection systems are compatible with NCDOT's signal controllers and can provide the same operation-critical inputs to the controllers as can magnetic induction loops. Yet they have numerous benefits enumerated herein.

The motivation for this study is to assess the costs of non-intrusive vehicle detection systems so that NCDOT can compare their costs and the value of the benefits they provide to the cost and benefits of magnetic induction loops. The goal is to determine how to quantify the various benefits and costs. The ITS and Traffic Signals Section has identified radar and cameras as the most promising non-intrusive detection technologies. The Signals Management Program Plan update now identifies the need to make a dedicated study of the costs, benefits, and return on investment of these technologies. Their goal is to identify specific technologies that meet specifications and to determine whether a targeted or blanket shift to those technologies is the optimal use of Department financial and time resources.

The purpose of this research is to assess NCDOT traffic signal detection technology to determine if it should evolve from the current magnetic induction loops to a technology based on radar or cameras (so that signal system performance is maintained or enhanced) and determine the cost of transitioning partially or fully. This study to answer the following questions: (1) “what technology is most well suited to NCDOT's needs?" (2) “what is the cost of the transition?" (3) “what is the return on investment? (4) “how would the transition occur?" This study will explore whether or not used by any division within NC and be realistically and efficiently implemented.

The lessons learned from the assessment and analysis will be combined with the experiences of other NC divisions and with knowledge gained from the literature to formulate one or more potential strategies to meet NCDOT needs. In doing so, NCDOT may improve both its overall financial decision making and the management of this critical roadway asset, resulting in overall cost savings and safety enhancement.

The proposal articulated below first introduces the context of the research need statement by providing a background. The background describes the nature of the NC traffic signal system. It then provides quantification data. The introduction also addresses the technology of vehicle detection systems and describes those that are most prominent. For each detection technology a table provides examples of manufactured products followed by a brief assessment of each technology.

The research needs and objectives are stated next and are followed by a literature review. This introductory literature review illustrates a number of important references that aid in understanding what others have done to address the questions mentioned above. The research tasks are then enumerated in significant detail. The significance of the proposed work (and the execution of the enumerated tasks) is stated. The research products are articulated next and a detailed discussion of the implementation plan is presented. That is followed by a cost benefit analysis which is a major part of the proposed work.

Finally, the project schedule is presented in two parts. The first part focuses on major milestones. These are a point in time. In one sense, they act as deadlines. The second schedule articulates the research tasks as activities. These span a longer duration over time.​]]></description>
      <pubDate>Thu, 18 Sep 2025 00:48:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2601431</guid>
    </item>
    <item>
      <title>SPR-5021: Traffic Signal Freight Prioritization via Vehicle to Infrastructure (V2I) Communications</title>
      <link>https://rip.trb.org/View/2576723</link>
      <description><![CDATA[This project will development and evaluate traffic signal freight prioritization utilizing third party in-cab alerts provider. This V2I application will communicate with the signal controller to extend green time for slower moving freight entering the decision zone with the objective of reducing hard braking and slower start-ups at intersections along the corridor. The communication latency (and needs) will be evaluated. This project will use and evaluate, commercial cellular infrastructure for both the communication to the trucks (in cab devices provided by Drivewyze) and communication to the traffic signal cabinets (INDOT cellular modems).]]></description>
      <pubDate>Tue, 15 Jul 2025 15:48:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2576723</guid>
    </item>
    <item>
      <title>Mitigation of Cybersecurity Vulnerabilities for Traffic Control Infrastructure</title>
      <link>https://rip.trb.org/View/2548649</link>
      <description><![CDATA[Objectives and associated tasks of the project are as follows: (1) Develop specifications for traffic controllers to mitigate the vulnerabilities found in BDV25-977-70 and recommend minimum requirements of cybersecurity for traffic signal controllers. (2) Develop a testing procedure and guidelines for specification testing. (3) Provide support in cybersecurity testing of traffic controllers and establish the procedure for testing. (4) Support 
Florida Department of Transportation (FDOT) in responsible disclosure with traffic controller manufacturers so that the vulnerabilities can be disclosed and a realistic timeline for mitigation can be implemented. (5) Assess other devices used in traffic management (e.g., traffic controllers, MMUs/CMU).]]></description>
      <pubDate>Wed, 30 Apr 2025 07:50:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548649</guid>
    </item>
    <item>
      <title>Network Level Proactive Traffic Operations Indicator (NPTOI) Using Artificial Intelligence (AI) and Connected Vehicle Data Integration</title>
      <link>https://rip.trb.org/View/2529964</link>
      <description><![CDATA[This project will focus on developing an extensive and implementable artificial intelligence (AI)-driven Network Level Proactive Traffic Operations Indicator (NPTOI) system aimed at mitigating urban traffic congestion and delays through effective and proactive prediction and prevention of traffic disruptions. The system will be built on real-time sensor-based and connected vehicle data, including various crowdsourced data from platforms such as the newly released Streetlight connected vehicles data, Lytx, LYNX, etc. and infrastructure-based data from Automated Traffic Signal Performance Measures (ATSPM), and as needed Microwave Vehicle Detection Systems (MVDS) and data from other available sensors, e.g., Close Circuit TVs (CCTVs), that could be used for validation or augment crowdsourced data. The NPTOI system can be integrated into existing traffic management infrastructure through testing and validation. NPTOI can also be used in a dashboard system that evaluates change over time and alerts operators to changes in the field that may affect traffic operations. Additionally, the University of Central Florida (UCF) team will analyze and report on the most effective Connected Vehicles data types for enhancing Florida Department of Transportation (FDOT) operations. Machine Learning (ML) methods such as Graph Neural Networks (GNN) and other techniques will be deployed to make such predictions since several ML algorithms are able to capture spatial and temporal features. Various data sources would be explored, and a combination of the sources will be experimented to obtain the best output predictions. The expected outcome of the research would enable FDOT to transition to AI-driven analysis reports that can screen network level traffic to give mobility indicators. The expectation is that such metric can enable operators to make decisions that can alleviate congestion.]]></description>
      <pubDate>Fri, 28 Mar 2025 08:00:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2529964</guid>
    </item>
    <item>
      <title>Evaluating the Interoperability of Connected and Autonomous Vehicles and Signal Phasing and Timing Infrastructure</title>
      <link>https://rip.trb.org/View/2507248</link>
      <description><![CDATA[Connected and autonomous vehicles (CAVs) leverage advanced technologies to integrate driving systems that enable communication between vehicles, infrastructure, and surrounding traffic conditions, while also being capable of self-driving. CAVs use a combination of sensors, cameras, radar, and lidar to perceive their environment, while advanced algorithms and artificial intelligence (AI) enable them to make driving decisions without human intervention. The signal phase and timing (SPaT) message is a crucial component of CAV communication, facilitating connectivity between vehicles and infrastructure. When vehicles are moving on the road, the movements of the following vehicle should be subject to the speed of the leading vehicle. SPaT at the intersections provides real-time traffic signal information, such as the current state (red, yellow, green) and the timing of signal changes, to connected vehicles. This signal information allows CAVs to optimize their driving behavior, adjust speeds, reduce idling time, and improve fuel efficiency by anticipating signal changes. The V2I Deployment Coalition (now named the Cooperative Automated Vehicle Coalition, CATC) announced the SPaT Challenge in 2016 as a call to state and local Departments of Transportation (DOTs) to integrate dedicated short-range communication (DSRC) technology (5.9GHz radio range) in approximately 20 intersections in all 50 states by 2020. As of early 2020, 26 states including Nebraska have responded to the challenge, with over 2,000 total DSRC signals planned for installation through the end of the year. The CATC report also indicated the benefits of SPaT deployment including increasing safety, efficiency and reliability of the public transportation network, decrease in fuel consumption, and improve overall speed adjustments.]]></description>
      <pubDate>Mon, 10 Feb 2025 14:04:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2507248</guid>
    </item>
    <item>
      <title>Development of a Scalable, Low-Cost, Environmentally-Friendly Adaptive Traffic Signal Control (SLE-ATSC) System</title>
      <link>https://rip.trb.org/View/2495005</link>
      <description><![CDATA[As an enhanced method for vehicle detection at signalized intersections, it is possible to use vehicle-probe data from smartphones, Global Navigation Satellite System (GNSS) receivers, and other types of mobile devices to complement existing traffic sensing and signal control, resulting in lower energy consumption. Using these additional data, it is now possible to estimate reliable traffic queue lengths at high-density traffic intersections. Given real-time reliable traffic queue lengths, it is possible then to dynamically adjust the signal phase and timing of an intersection, with the goal of minimizing traffic queues, waiting times, and energy use. Using UC Riverside’s Innovation Corridor as a target arterial roadway, the research team will develop a scalable, low-cost, environmentally-friendly adaptive traffic signal control (SLE-ATSC) system based on receiving real-time traffic data from sources such as TomTom and INRIX. The signal control system will be implemented for several of the key intersections along the corridor, using a calibrated state-of-the-art traffic simulation platform. Various metrics will be evaluated, comparing the existing traffic signal phase and timing to the new dynamic signal phase and timing resulting from the adaptive signal control system. Using the calibrated simulation model, traffic system metrics will be estimated. In addition, part of the research team (TSU) will utilize their driving simulators as part of a “Hardware-in- the-Loop” testing system for the proposed adaptive traffic signal control system. The traffic simulation model developed at UCR will interface directly with the TSU driving simulators, allowing the research team to see more realistic driving behavior operating in the simulation platform. This will provide more realistic measures of the overall system performance, with a focus on safety, mobility, and environmental metrics.]]></description>
      <pubDate>Fri, 31 Jan 2025 16:35:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495005</guid>
    </item>
    <item>
      <title>Safety Impacts of Signal System Retiming to Improve Progression</title>
      <link>https://rip.trb.org/View/2494743</link>
      <description><![CDATA[North Carolina Department of Transportation (NCDOT) owns and maintains coordinated traffic signal systems throughout the state in primarily rural and suburban areas. NCDOT’s Signal System Timing and Operations office assists the local Divisions with the signal system retiming program to update traffic signal settings of existing coordinated corridors. This program has primarily focused on identifying locations with the input of Division Engineers and operational summary data to prioritize the corridors updated each year.

To date, with the ability of signalized intersections to manage vehicle and multimodal conflicts, the crash rate has been significantly reduced compared to the past. However, as traffic volume continuously increases, drivers who arrive at a signal at the end of the green period are very likely to speed up to proceed to the intersection. This yields crash hotspots at signalized intersections, which could increase if traffic is not able to progress
smoothly. According to the Federal Highway Administration (FHWA, 2023), about one-third of crashes occurred at signalized intersections, among which rear-end crashes and right-angle crashes are the most common types. Therefore, minimizing the safety risk at signalized intersections has been an important issue for DOTs to address in the last century, with many potential treatments depending on the issues identified.

In recent decades, signal coordination has been utilized as a method to effectively improve the performance of the traffic control system, especially in high density urban areas. It has been widely used as a measure to mitigate congestion, reduce travel time, and eliminate travel delays. On the other hand, however, it may change the traffic flow feature at intersections, which could lead to potential safety issues. As signal coordination has been extensively implemented into urban signalized arterials, safety concerns were unsurprisingly raised by both transportation engineers and the public. Since traffic coordination may result in higher mainline speeds than non-coordinated conditions, most of these opinions came up with the worries that a higher speed may increase the risk of being involved in a traffic crash, particularly injury or fatal crashes. Inversely, improved progression of traffic may reduce the total crash count by reducing rear end collisions that can occur in start-stop traffic. To date, there is neither solid  theoretical-level models to analyze this issue, nor solid evidence from the field to support the concern. In this regard, this research effort aims to assess the effects of traffic signal coordination on the safety performance of North Carolina coordinated arterials. The research is anticipated to develop predictive model(s) that can estimate or predict crashes based on the operational or site characteristics of North Carolina signalized corridors. ]]></description>
      <pubDate>Tue, 21 Jan 2025 10:22:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2494743</guid>
    </item>
    <item>
      <title>Consideration of "Recall-Rest-Walk" During Signal Coordination</title>
      <link>https://rip.trb.org/View/2486927</link>
      <description><![CDATA[Long-time Utah Department of Transportation (UDOT) traffic signal practice has been to place pedestrian phases parallel to the mainline in “pedestrian recall” and “rest-in-walk” for coordinated signals in a corridor during signal coordination. This means that in many cases the walk indication (for crossings of the side streets) comes on every time and stays on for most of the time during the main street thru green interval, during daytime hours, without the need for actuation (pedestrian detection/push-button use). This technique is also sometimes described as maximizing the length of the walk interval, and it is designed to make use of “leftover” time associated with a parallel vehicular phase (beyond the minimum needed to serve the pedestrian crossing), thus hoping to reduce pedestrian delay, increase pedestrian signal compliance, and accommodate slower pedestrians without significantly constraining a signal’s vehicular operations and cycle. The objective of this research project is to consider the safety and operational implications (advantages and disadvantages) of “recall-rest-walk” pedestrian phasing at coordinated traffic signals in Utah. The project will offer recommendations for UDOT traffic signal timing/phasing practices regarding pedestrians, as well as recommendations for future monitoring and research. ]]></description>
      <pubDate>Sun, 05 Jan 2025 21:43:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2486927</guid>
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