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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>MARL-based cooperative transit signal priority for the arterial road to reduce schedule delay</title>
      <link>https://trid.trb.org/View/2643295</link>
      <description><![CDATA[Transit signal priority (TSP) is an effective strategy to reduce transit delays and improve intersection efficiency. This paper introduces a Cooperative TSP strategy of Variable phase (CTSPV) using multi-agent reinforcement learning (MARL) to minimize transit schedule delays on arterial roads. The agents adjust phase sequences and durations based on real-time traffic, balancing transit and non-transit vehicle needs, resolving conflicting bus requests, and ensuring agent cooperation. Invalid action masking ensures compliance with green time and phase-skipping rules. Simulation results show CTSPV reduces person delay, queue lengths, and lateness by 8.7%, 31.6%, and 17.0%, respectively, compared to fixed-time signals. Testing different green time constraints highlights the importance of proper restrictions for efficient learning. Analysis of CTSPV's signal timing reveals agents prioritize phases with high traffic demand and bus priority, skipping phases with lower demand. Evaluation results of generalized rule-based strategies based on those RL-derived patterns demonstrate the good performance of RL-learned knowledge.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643295</guid>
    </item>
    <item>
      <title>Utilizing the Internet of Things and Big Data for Traffic Management: The Role of Physical Network Systems and Collaborative Signal Light Control</title>
      <link>https://trid.trb.org/View/2610709</link>
      <description><![CDATA[In investigating the use of big data and the Internet of Things (IoT) for traffic control, this study emphasizes the value of physical network systems and cooperative signal light control. This paper proposes a novel giant armadillo optimized model predictive controller (GAO-MPC) method for dynamically controlling the phases and cycles of traffic lights in a remote intersection. The GAO strategy is applied to enhance the parameters of the MPC. The goal of the effort is to create an enhanced, adaptable technology that could be acquired off-the-shelf, eschewing intricate and costly computational techniques that would make it more difficult to use in real-world situations. An IEEE 802.15.4-based WSN (Wireless Sensor Network) during actual traffic surveillance is combined into several MPCs, one for every stage, that operate simultaneously to create the suggested traffic light adaptive management system. Every MPC controls the turning motion of automobiles and continually alters the traffic light’s phase and green period. The suggested system integrates the pros associated with employing simultaneous MPCs enhanced effectiveness, tolerance for faults, and assistance for phase-specific management with the advantages of the WSN, including ease of installation and management, inexpensiveness, and adaptability. The GAO-MPC approach improves traffic flow, decreases vehicle paths, and adjusts dynamically to different traffic situations, making it superior to conventional fuzzy logic controller (FLC) and programmable logic controller (PLC) systems. The suggested technique outperforms the current alternatives in published research and opens the door for increased adoption because it drastically cuts down on car queues.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:10:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610709</guid>
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    <item>
      <title>Public Acceptance Scoring System for Alternative Intersections</title>
      <link>https://trid.trb.org/View/2672682</link>
      <description><![CDATA[Intersections are key components of road infrastructure, with conventional designs being the most common. However, these designs might face capacity and safety issues, prompting the need for alternatives. Alternative intersections, such as reduced conflict intersections (RCIs) have shown improvements in travel time and safety for both vehicles and pedestrians, but public acceptance remains a challenge. This paper introduces the Public Acceptance Scoring System (PASS), a tool designed to evaluate the public acceptance of alternative intersection designs. PASS prioritizes variables such as driver confusion, business impacts, and pedestrian discomfort. Based on a review of existing studies and input from experts at the North Carolina Department of Transportation (NCDOT), 16 key variables were identified. The results show that newer designs, like the seven-phase and partial median U-Turn (MUT) with a three-phase traffic signal, are likely to receive higher public acceptance, while partial Continuous Flow Intersections (CFI) may struggle due to traffic redirection and business access limitations.]]></description>
      <pubDate>Thu, 26 Mar 2026 09:06:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672682</guid>
    </item>
    <item>
      <title>Assessing impacts of traffic signal countdown timers on safety and efficiency at signalized intersections</title>
      <link>https://trid.trb.org/View/2652317</link>
      <description><![CDATA[Traffic Signal Countdown Timer (TSCT) displays the remaining times of green, yellow, and red intervals at a traffic signal. While TSCT has widely been implemented in various countries, there is a lack of studies that comprehensively assess the effects of TSCT on traffic safety and efficiency using a simulation based on understanding of changes in driver behavior in different signal phases in the presence of TSCT.  This study investigates the impacts of TSCT on traffic safety and efficiency at signalized intersections with high truck volume along the Huron Church Road in Windsor, Ontario, Canada. Driver behavior and traffic flow were simulated using PTV Vissim for the four scenarios − no-timer, Green Signal Countdown Timer (GSCT), Red Signal Countdown Timer (RSCT), and a combination of GSCT and RSCT (GSCT + RSCT). Based on the observational data from previous field studies, changes in driver behaviors in the presence of TSCT were replicated by dynamically adjusting the simulation parameters in different signal phases using Vissim-COM interface.  The Crash Potential Index (CPI) decreased by 37% while the speed of the road network increased by 6% in the GSCT + RSCT scenario compared to the no-timer scenario. The RSCT reduced the CPI more than the GSCT but did not significantly improve the traffic efficiency. The increase in speed near the intersection during the green phase was observed in the GSCT scenario, whereas smoother deceleration rate of approach vehicles during the red phase was observed in the RSCT scenario. Due to these changes, the average number of vehicles entering the intersection during the green phase in each cycle increased in the GSCT + RSCT scenario. Moreover, the TSCT helped cars avoid rear-end conflicts and increased truck speed.  Both GSCT and RSCT can be implemented to improve traffic safety and efficiency at signalized intersections.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:43:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652317</guid>
    </item>
    <item>
      <title>Traffic signal coordinated control model for long arterial based on traffic flow spatiotemporal characteristics</title>
      <link>https://trid.trb.org/View/2633411</link>
      <description><![CDATA[Traditional signal coordination methods face challenges in ensuring efficient traffic flow on long arterials due to urban expansion and complex spatiotemporal variations. However, existing methods struggle to achieve effective signal coordination under complex spatiotemporal variations, and lack methodological framework for universally applicable green wave coordination. To address this, a spatiotemporal partitioning-based green wave trajectory feature coordination optimization model is proposed. First, temporal partitioning is performed using an improved Fisher optimal segmentation method, while spatial subarea division is achieved via an enhanced K-Medoids algorithm. For each subarea, an arterial traffic signal control model is established based on green wave trajectory characteristics. Phase difference coordination equations are then applied to synchronize adjacent subareas. The model is validated on Foshan’s Lvjing Road, with evening peak performance compared against a classical green wave trajectory approach. Results indicate that the proposed model reduces vehicle average delay by 13.18% and the number of stops by 18.05%.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633411</guid>
    </item>
    <item>
      <title>A Sequential Signal Timing Schemes Inference Framework Using Automatic Vehicle Identification Data</title>
      <link>https://trid.trb.org/View/2613027</link>
      <description><![CDATA[Signal timing parameters (e.g., phase sequence, cycle length, and phase duration) are crucial components in traffic signal control schemes. It affects not only traffic management but also traffic safety. However, obtaining the sequential signal timing schemes at intersections is challenging, primarily due to confidentiality issues within the traffic management department. As a result, it becomes difficult to effectively evaluate intersection conditions. To solve this issue, this study attempts to develop a sequential signal timing schemes inference framework based on Automatic Vehicle Identification (AVI) data. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to infer the phase sequence. Then, the cycle length and cycle distribution within a day are estimated by the Fast Fourier transform (FFT) spectrum. An improved firefly algorithm (IFA), incorporating mutation operations and a dispersal mechanism, obtains a more precise signal phase duration. The final sequential signal timing schemes is determined by fitting the square wave diagram of phase duration to the scatter plot of AVI data and calculating their overlap. To testify to the performance of the proposed framework, a real-world AVI data set from Jinan, China, is applied. Experimental results show that the proposed method performs more efficiently on parameter inferences than the baseline approach. The relative error between the inference and the actual cycle results is 2.03%. This work is highly significant in intelligent transportation systems, such as intersection evaluation and navigation assistance.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613027</guid>
    </item>
    <item>
      <title>A lane-based intersection design model for lane assignment and signal timing under mixed traffic of human-driven and self-driving vehicles with dedicated lanes</title>
      <link>https://trid.trb.org/View/2609513</link>
      <description><![CDATA[This paper proposes a method of dedicated lanes and phases for self-driving vehicles at intersections under a heavy mixed traffic flow of connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). With the gradual maturation of connected and autonomous driving technology, the mixed traffic flow of CAVs and HDVs will become an inevitable trend. Given a series of issues such as safety, social recognition, road rights that will arise after the large-scale deployment of CAVs on the road at early stages, physical separation between CAVs and HDVs on roads could be essential in real-world applications. Based on this, aiming at intersections with heavy mixed flows, we employ CAV dedicated lanes and phases, and establish a lane-based optimal design model that optimizes the intersection by choosing lane configuration (spatial design) and signal timing (temporal design) to maximize the intersection efficiency represented by reserve capacity. The model is further extended to the situation where CAV dedicated waiting areas are deployed. By setting 0–1 variables to describe the conflict between different traffic flows, we formulate the optimization problem as a binary mixed-integer linear program, and the global optimal solution can be readily determined by using the branch-and-cut method. Results from our numerical experiments show that, for intersections with relatively high CAV flows, the higher the penetration of CAVs, the greater the reserve capacity in most scenarios tested, even though CAVs have only one lane. Additionally, CAV waiting areas do not necessarily improve intersection efficiency. Although CAVs can form platoons accelerating at the same time in the dedicated lane, the flexibility of overlapping phases is undermined since phases are partially fixed due to waiting area deployment, resulting in reduced capacity.]]></description>
      <pubDate>Fri, 09 Jan 2026 16:59:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2609513</guid>
    </item>
    <item>
      <title>Inference of signal phase and timing with low penetration rate vehicle trajectories</title>
      <link>https://trid.trb.org/View/2597133</link>
      <description><![CDATA[Traffic signals are a crucial component of urban traffic networks, and signal phase and timing (SPaT) information serves as an essential input for various urban traffic operational applications. Obtaining SPaT information on a large scale is challenging due to the diversity of traffic signal controllers from different manufacturers and jurisdictions. With the advent of broadly defined connected vehicles, vehicle trajectories can be leveraged to estimate SPaT information since they are directly controlled by traffic signals. Although some existing studies have proposed methods for estimating SPaT information using vehicle trajectory data, most are limited to fixed-time traffic signals. To address this limitation, this paper proposes a suite of SPaT inference algorithms applicable to both fixed-time and responsive signals. With only low penetration rate vehicle trajectory data as input, the inference program can estimate the complete SPaT information for traffic signals with fixed cycle lengths and the average cycle/splits for those with time-varying cycle lengths. The proposed method is validated through case studies at real-world intersections.]]></description>
      <pubDate>Mon, 24 Nov 2025 15:30:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2597133</guid>
    </item>
    <item>
      <title>Towards fair lights: A multi-agent masked deep reinforcement learning for efficient corridor-level traffic signal control</title>
      <link>https://trid.trb.org/View/2588392</link>
      <description><![CDATA[This study presents an adaptive traffic signal control (ATSC) method for managing multiple intersections at the corridor level by proposing a novel multi-agent masked deep reinforcement learning (DRL) framework. The method extends the hybrid soft-actor-critic architecture to optimize green light timings for intersections across a corridor network, fostering a balance between vehicle flow and pedestrian movements with an emphasis on humanism, fairness, and equality. By integrating an innovative phase mask mechanism, the authors' model dynamically adapts to the fluctuating demand of different transportation modalities by discovering new states or actions that could avoid local optima and achieve higher rewards. The authors comprehensively test their method using five naturalistic traffic scenarios in Melbourne, Australia. The results demonstrate a significant improvement in reducing the number of impacted travellers compared to existing DRL and other baseline methods. Furthermore, the inclusion of the phase mask mechanism enhances the authors' model's performance through ablation analyses. The proposed framework not only supports a fairer traffic signal system but also provides a scalable, adaptable solution for diverse urban traffic conditions. .]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2588392</guid>
    </item>
    <item>
      <title>AI-Enabled Vision System for Intersection Analytics</title>
      <link>https://trid.trb.org/View/2607189</link>
      <description><![CDATA[This project developed and demonstrated an AI-enabled vision system for intersection analytics capable of automatically generating Turning Movement Counts (TMCs) and integrating them with Signal Phase and Timing (SPaT) data to evaluate traffic signal performance. Using existing Missouri Department of Transportation (MoDOT) CCTV (Closed Circuit Television) infrastructure, the system eliminated the need for manually placed detection zones and significantly reduced the labor traditionally required for data collection.]]></description>
      <pubDate>Thu, 16 Oct 2025 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2607189</guid>
    </item>
    <item>
      <title>Sequencing for Phases with Flashing Yellow Arrow</title>
      <link>https://trid.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://trid.trb.org/View/2608463</guid>
    </item>
    <item>
      <title>Evaluation of signal phasing and timing plans for mixed traffic condition based on information entropy</title>
      <link>https://trid.trb.org/View/2594469</link>
      <description><![CDATA[Signalized intersections in China have long experienced traffic congestion and accidents due to mixed traffic. Increasing car ownership in developing countries is inevitable, leading to worsening traffic congestion and emissions. However, high construction costs and legal restrictions have impeded traditional road infrastructure expansion and improvement projects. As a result, transportation authorities, particularly in developing countries, face the significant challenge of finding cost-effective traffic management solutions to mitigate traffic congestion and emissions. Signal control is considered a crucial method for optimizing traffic flow. This study examines the effects of diverse signal control strategies on signalized intersections from a microscopic perspective, focusing on optimal fixed signal timing and available road traffic resources. To effectively assess the intersection operation state under different signal control strategies, this study introduces the concept of information entropy from physics. The proposed evaluation system can be directly applied to existing road infrastructure. This system provides a clearer understanding of the most effective signal control strategy for signalized intersections in a mixed-traffic environment, while also maintaining the current road traffic resources. This study introduces a novel signal control evaluation system based on information entropy theory, providing transportation management with a scientific decision-making tool. This approach significantly optimizes signal timing plans within mixed traffic environments under existing road resource constraints. Consequently, it effectively alleviates congestion, reduces accidents and emissions, and ultimately maximizes traffic resource utilization, thereby promoting sustainable development of the transportation system.]]></description>
      <pubDate>Tue, 07 Oct 2025 08:21:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594469</guid>
    </item>
    <item>
      <title>Research Project Name: Development of a CAV Testbed-enhanced Smart Campus at Morgan State University - Phase III</title>
      <link>https://trid.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://trid.trb.org/View/2606401</guid>
    </item>
    <item>
      <title>Utah Department of Transportation SMART Grant Stage 1 Enabling Trust and Deployment Through Verified Connected Intersections Dataset [supporting dataset]</title>
      <link>https://trid.trb.org/View/2593180</link>
      <description><![CDATA[This dataset contains a series of folders for 8 tested connected intersection (CI) in the reference implementation corridor of the Utah Department of Transportation's Strengthening Mobility and Revolutionizing Transportation (SMART) Grant Phase 1 effort with the United States Department of Transportation. Each tested CI contains the data collected at the CI for Signal Phase and Timing (SPaT) testing and the output reports from the SPaT validation tool (described in the 2024 Crash Avoidance Metrics Partnership (CAMP) 'Assessment of SPaT Accuracy to Support RLVW Application' report), as well as the data collected at the CI for MAP testing and the output reports from the MAP validation tool (described in the 2024 CAMP 'Assessing Node Point Accuracy in the SAE J2735 MAP Message' report). Each folder and file contains the locally assigned values for the intersection it is associated with: 7704, 7705, 7706, 7707, 7708, 7709, 7710, 7720.]]></description>
      <pubDate>Wed, 10 Sep 2025 09:22:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593180</guid>
    </item>
    <item>
      <title>Enabling Trust and Deployment Through Verified Connected Intersections [Data Management Plan]</title>
      <link>https://trid.trb.org/View/2593181</link>
      <description><![CDATA[This project will create a mechanism for original equipment manufacturers (OEMs) to trust that connected intersections (CIs) deployed by infrastructure owner operators (IOOs) are broadcasting accurate, consistent, reliable, and secured messages that can support in-vehicle RLVW and other safety applications. Without a reproducible process to verify CIs, a coupling of this verification process to the issuance of security credentials, a process for detecting misbehavior and re-testing intersections, and a field deployment demonstrating verified broadcasts, production vehicles with these life-saving applications will be unable to operate. The five project goals for this effort are: (1) Complete a successful reference implementation corridor. (2) Develop a process for OEMs to trust CIs to have accurate, consistent, reliable, secure messages. (3) Establish ongoing collaboration between infrastructure owner operators (IOOs), OEMs, and Security Credential Management System (SCMS). (4) Conduct outreach and work with other deploying IOOs. (5) Make test tools, procedures, and verification processes publicly available.]]></description>
      <pubDate>Wed, 10 Sep 2025 09:22:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593181</guid>
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