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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>Evaluating the Safety Effects of Automated Traffic Signal Performance Measures</title>
      <link>https://trid.trb.org/View/2681403</link>
      <description><![CDATA[This report presents two methods for using crash modification factors (CMFs) to evaluate the safety effects of automated traffic signal performance measures (ATSPMs) based on changes in signal timing or operation. The first method produces an estimate of the change in crash frequency and crash cost associated with implementing an ATSPM-based approach to signal timing and operations within a signal system. The second method uses CMFs to quantify the change in crash frequency and severity associated with a specific ATSPM-based change to signal timing or operation at one or more signalized intersections (e.g., the safety effect of reducing red light running). The methods were developed based on data from arterials and signalized intersections in several states over multiple years. This report will be of interest to state departments of transportation, local transportation agencies, and consultants involved in planning, managing, maintaining, or operating traffic signals utilizing ATSPMs.]]></description>
      <pubDate>Sun, 22 Mar 2026 17:18:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681403</guid>
    </item>
    <item>
      <title>Communication-free Distributed Control Algorithm for autonomous vehicles at intersections</title>
      <link>https://trid.trb.org/View/2594527</link>
      <description><![CDATA[This paper introduces a novel approach for managing autonomous vehicles at signal-free intersections through a Communication-free Distributed Control Algorithm (CfDCA). Unlike centralized systems or communication-based decentralized methods, CfDCA relies solely on onboard sensors and in-vehicle decision-making to ensure efficient and collision-free navigation. The algorithm formulates intersection management as a distributed optimization problem with demonstrated safety logics and robustness to measurement errors. The algorithm combines a dynamic resource acquisition graph with a refined priority function and an adaptive tolerance mechanism to ensure efficient performance under varying traffic conditions. A stochastic tie-breaking mechanism is proposed to handle rare cases of identical priorities, while deadlock prevention is guaranteed through strict priority ordering. Simulation experiments demonstrate that CfDCA reduces average delay and queue length and is able to achieve throughput higher than actuated signalized intersections and outperforms a first-come-first-served baseline in delay reduction. Additionally, the algorithm’s distributed design offers scalability and eliminates dependency on communication infrastructure.]]></description>
      <pubDate>Thu, 20 Nov 2025 17:06:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594527</guid>
    </item>
    <item>
      <title>Estimation of PCE Factors for Traffic at Signalized Intersections for Capacity Analysis in Monrovia</title>
      <link>https://trid.trb.org/View/2571598</link>
      <description><![CDATA[Estimating passenger car unit (PCU) values in developing countries is crucial for traffic analysis and capacity planning. However, the unique traffic mix, driver behavior, and infrastructure challenges in these countries make traditional approaches used in developed nations unsuitable. Heterogeneous traffic, consisting of a wide variety of vehicle types and poor lane discipline, further complicates intersection performance evaluation. Conventional methods struggle to handle this complexity due to data limitations and difficulties in measurement. To address these challenges, researchers are turning to unmanned aerial vehicles (UAVs) or drones to streamline data collection and extraction. In this study, three selected signalized intersections were analyzed using UAV monitoring data in the city of Monrovia. Namely, the Capitol Bypass intersection, Boulevard Junction, and Neezoe Junction. The average speed and area occupancy method was employed. A Multiple Linear Regression model was developed to model the PCUs for different vehicle categories at these intersections. Results from the analysis revealed that the PCU values for two-wheelers, three-wheelers, taxis, minibuses, Light Commercial vehicles, and medium trucks are 0.23, 0.60, 1.18, 1.21, 1.59, and 2.00 respectively. The PCU values obtained and the modeled equations in this study can be used as a guideline in the traffic analysis and performance evaluation at signalized intersections in the city of Monrovia and generally in other counties in the Republic of Liberia.]]></description>
      <pubDate>Mon, 29 Sep 2025 08:35:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571598</guid>
    </item>
    <item>
      <title>Regional Traffic Signal Performance Measures Final Implementation Report</title>
      <link>https://trid.trb.org/View/2561914</link>
      <description><![CDATA[The Mid-America Regional Council (MARC) and its program area, Operation Green Light (OGL), manage more than 750 signalized intersections across a nine-county region. Kansas City Regional Signal System OGL staff members analyze and retime corridors when they notice the need for potential improvements, but this awareness is often delayed because of incomplete or untimely data, lack of skills or resources to analyze available data, or inadequate resources to collect new data. Ideally, each agency and traffic signal would be equipped with advanced equipment and functioning detection to support automated traffic signal performance measures (ATSPMs) however, this infrastructure is too costly for all agencies to install and maintain. Some agencies have installed such infrastructure, but disadvantaged communities face noteworthy challenges in terms of staffing and infrastructure, leading to distinct differences in signal management across the Kansas City metro area. A lack of data and resources in any agency leads to inefficiencies in task prioritization, stretching staff time and negatively affecting roadway operations. The two primary needs for OGL and its partners are to better understand and communicate the status of the Kansas City Regional Transportation System. Stage 1 of the Regional Traffic Signals Performance Measures project is part of the Strengthening Mobility and Revolutionizing Transportation (SMART) program’s smart technology traffic signals category. This stage evaluates the capability of four vendor systems to provide data and analytics platforms for understanding and communicating corridor performance along six major routes.]]></description>
      <pubDate>Tue, 17 Jun 2025 09:43:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561914</guid>
    </item>
    <item>
      <title>Traffic Signal Performance Measures and Analysis [supporting dataset]</title>
      <link>https://trid.trb.org/View/2561915</link>
      <description><![CDATA[The Mid-America Regional Council (MARC) and its program area, Operation Green Light (OGL), manage more than 750 signalized intersections across a nine-county region. Kansas City Regional Signal System OGL staff members analyze and retime corridors when they notice the need for potential improvements, but this awareness is often delayed because of incomplete or untimely data, lack of skills or resources to analyze available data, or inadequate resources to collect new data. Ideally, each agency and traffic signal would be equipped with advanced equipment and functioning detection to support automated traffic signal performance measures (ATSPMs) however, this infrastructure is too costly for all agencies to install and maintain. Some agencies have installed such infrastructure, but disadvantaged communities face noteworthy challenges in terms of staffing and infrastructure, leading to distinct differences in signal management across the Kansas City metro area. A lack of data and resources in any agency leads to inefficiencies in task prioritization, stretching staff time and negatively affecting roadway operations. The two primary needs for OGL and its partners are to better understand and communicate the status of the Kansas City Regional Transportation System. Stage 1 of the Regional Traffic Signals Performance Measures project is part of the Strengthening Mobility and Revolutionizing Transportation (SMART) program’s smart technology traffic signals category. This stage evaluates the capability of four vendor systems to provide data and analytics platforms for understanding and communicating corridor performance along six major routes. This dataset contains corridor and intersection performance measures data and analysis from four different data sources. The dataset also includes blank evaluation forms used to evaluate different scenarios.]]></description>
      <pubDate>Tue, 17 Jun 2025 09:43:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561915</guid>
    </item>
    <item>
      <title>Safety validation for connected autonomous vehicles using large-scale testing tracks in high-fidelity simulation environment</title>
      <link>https://trid.trb.org/View/2522604</link>
      <description><![CDATA[Public concern over the implementation of Connect Autonomous Vehicles (CAVs) remains a significant issue, and safety validation for CAVs remains a critical challenge due to the limitations of existing testing methods. While real-world testing is crucial, it can be expensive, time-consuming, and potentially impractical for evaluating the operation of CAV fleets. This paper presents a comprehensive co-simulation framework integrating the fully compiled CARLA with traffic microsimulation to establish a large-scale (20 × 20 km2) testing environment for systematic CAV safety validation. The framework encompasses three key components: 1) a high-fidelity testing environment featuring diverse road geometries and dynamic conditions including weather variations and realistic traffic flows; 2) an intelligent CAV function developed through deep reinforcement learning and enhanced with utility-based connectivity strategies; 3) A sophisticated safety measurement metric that utilizes surrogate safety assessments, integrating a multi-type Bayesian hierarchical model to comprehensively evaluate risk factors and incident probabilities. The case study assessed CAV penetration rates ranging from 0 % to 100 %, identifying an optimal safety performance at a 70 % penetration rate, which resulted in an 86.05 % reduction in accident rates compared to conventional driving scenarios. This optimal safety level was effectively achieved in rural and suburban areas, where the average conflict probability was 0.4. However, in transition zones that connect high-, medium-, and low-density areas, significant traffic conflicts persisted even at this optimal penetration rate, with a conflict probability exceeding 0.7. Key results highlight critical safety patterns under optimal conditions, revealing that roundabouts and signalized intersections account for over 70 % of conflicts involving CAVs. This work advances CAV safety validation by providing a more realistic, large-scale testing environment that compensates for real-world testing limitations and allows for comprehensive safety evaluations across diverse scenarios.]]></description>
      <pubDate>Mon, 14 Apr 2025 17:08:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2522604</guid>
    </item>
    <item>
      <title>Prediction of pedestrian exposure to traffic particulate matters (PMs) at urban signalized intersection</title>
      <link>https://trid.trb.org/View/2471453</link>
      <description><![CDATA[To evaluate the prediction performance of three models (CAL3QHC – California Line Source Model 3 with Queuing and Hot Spot calculations, BPNN - Back Propagation Neural Network, and WNN - Wavelet Neural Network) on pedestrian exposure to traffic-related particulate matter (PM), the models were applied for evaluating PM₂.₅ concentrations, and an investigation was performed using mobile measurement devices, at one of the busiest signalized intersections in Xi’an, China. The results revealed that the concentrations of fine particles (PM₂.₅) at the intersection were highest in the southeast and northeast corners, and that the spatial distribution of PM is related to wind and the layout of buildings around. Additionally, the results indicated that the concentration of PMs during the weekends was lower than that for weekdays, and the PMs concentration in the off-peak period was slightly higher than in the morning peak period. Further detailed analysis showed that the WNN model could make better predictions for varied meteorology and traffic conditions, compared to the other two models. The prediction errors of PM₂.₅ for the CAL3QHC, BPNN, and WNN are 8.25 %, 5.55 %, and 4.61 %, respectively. The results suggest the WNN model is a useful and fairly accurate tool for predicting PM at signalized intersections.]]></description>
      <pubDate>Mon, 30 Dec 2024 09:57:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2471453</guid>
    </item>
    <item>
      <title>Real-time traffic conflict prediction at signalized intersections using vehicle trajectory data and deep learning</title>
      <link>https://trid.trb.org/View/2452460</link>
      <description><![CDATA[Real-time conflict prediction at signalized intersections is crucial for urban road safety management. This study developed a real-time conflict prediction framework for signalized intersections using video data real-time recognition technology and deep learning techniques, incorporating lane-level information and feature interactions. The modeling framework consists of three stages: real-time video data extraction and processing, the development of a Deep and Cross Network (DCN)-based real-time traffic conflict prediction model, and conflict-driven factor interpretability analysis through SHapley Additive exPlanations (SHAP). In the first stage, an efficient automated trajectory extraction system is designed to obtain vehicle trajectories in real time for dynamic traffic parameters and conflict frequency identification. In the second stage, a DCN model is developed to construct the relationships between dynamic traffic parameters, including their interactions, and traffic conflicts. In the third stage, SHAP explores the impact mechanisms of different dynamic traffic parameters on traffic conflicts. The model’s predictive performance and interpretability are evaluated using intersection video data from Changsha City, China. The results show that: (1) In real-time traffic conflict prediction at signalized intersections across different modified time-to-conflict thresholds (1.5 s and 3.0 s), the DCN model consistently outperformed statistical and machine learning models. (2) High traffic flow on main and secondary roads at signalized intersections significantly increases the complexity and frequency of conflicts, with varying sensitivity depending on the interaction of traffic flow, speed, and platoon length. (3) The proposed framework provides a safety measurement standard for data-driven road safety management methods.]]></description>
      <pubDate>Wed, 27 Nov 2024 13:42:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2452460</guid>
    </item>
    <item>
      <title>Adaptive Control Method of Multi-Phase Intersection Based on Traffic Status Identification</title>
      <link>https://trid.trb.org/View/2203692</link>
      <description><![CDATA[Adaptive signal control methods have the ability to improve operations at isolated intersections. However, most of these experiences mainly focus on one specific traffic status, so other traffic characteristics are not fully considered. In this paper, the traffic status of an isolated intersection is identified using a two-dimensional fundamental diagram divided into four levels. An adaptive traffic signal control method based on traffic status identification is proposed. Different signal control performance indexes (PIs) are chosen in different traffic levels. Appropriate evaluation parameters are selected for the PIs according to real-time traffic characteristics. An enumeration algorithm is put forward to solve the optimized model of PIs. Then, a signalized intersection in Shenzhen city is studied through theoretical calculation and simulation evaluation via a microscopic traffic simulation software, Paramics. The results indicate that the signal strategy derived from the proposed method is more effective than the original F. Webster method strategy, because the proposed PIs outperform those of F. Webster on all four traffic levels.]]></description>
      <pubDate>Fri, 19 Jul 2024 16:40:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203692</guid>
    </item>
    <item>
      <title>Planning Application for Mobility Assessment of Intersection Forms in Intersection Control Evaluation</title>
      <link>https://trid.trb.org/View/2362144</link>
      <description><![CDATA[The use of alternative intersections and interchanges (AIIs) has proliferated significantly over the past two decades. Their introduction is motivated by the need to improve mobility and safety at conventional signalized and unsignalized intersections. This paper presents a mobility assessment component of the intersection control evaluation (ICE). The focus of this component was to develop a planning application and computational tools for the Stage II mobility analysis that can fill the current gap in coverage of the diverse (and expanding) suite of AIIs. The design of the tool prioritized the simultaneous analysis of multiple AIIs within the same platform using a common input dataset. This feature of the tool guarantees that alternative forms can be compared side-by-side under the same set of inputs and parameter assumptions using overall system delay as the key performance measure. In addition, the research developed a framework and movement definition scheme that enables the extension of the current platform to accommodate new and yet to be conceived innovative intersection forms. A comparison against two widely used commercial analytical platforms yielded virtually identical system delays and level of service ratings across 17 case studies covering eight different AII forms and under varying intersection congestion levels. The maximum system delay difference between the two methods was about 5?s, with an average absolute difference of less than 2.5?s. Future work will focus on expanding the tool to unsignalized versions of AIIs along with new and innovative forms that appear on the horizon.]]></description>
      <pubDate>Fri, 05 Apr 2024 10:17:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2362144</guid>
    </item>
    <item>
      <title>A Graphical Approach to Automated Congestion Ranking for Signalized Intersections Using High-Resolution Traffic Signal Event Data</title>
      <link>https://trid.trb.org/View/2347365</link>
      <description><![CDATA[In recent years, high-resolution traffic signal event data has provided valuable insights into understanding and managing congestion at signalized intersections. While existing applications primarily employ automated traffic signal performance monitoring (ATSPM) systems as postanalysis tools for identifying everyday congestion causes, traffic engineers are increasingly overwhelmed by the number of ATSPM-capable intersections. The workload increases extensively as the number of ATSPM-capable intersections rises mainly due to the necessity of manually checking and generating performance figures. Nonetheless, an advanced ATSPM system capable of automatically detecting time-of-day congestion bottlenecks among multiple intersections and suggesting “top intersections of interest” would significantly aid traffic managers in monitoring historical congestion and preventing future congestion occurrences. This paper introduces an efficient graphical automated congestion ranking method for capable intersections, leveraging high-resolution traffic signal event data as the basis for automated congestion ranking. To accomplish these objectives, ATSPM concepts are built upon by continuously generating ATSPM measures of effectiveness (MOEs). Utilizing continuously generated ATSPM performance measures in Frisco, Texas, over several months, an efficient graphical method is devised for ranking hourly congestion levels among the studied ATSPM-capable intersections. All intersections are assessed and ranked using a multiobjective optimization technique, the Pareto front method. The points on the Pareto front represent dominating intersections with at least one inferior performance measurement, warranting prioritized improvement. The dominating points identified from the test dataset were validated and further explained using Purdue coordination diagrams (PCD), along with another individual dataset—Wejo-connected vehicle data. The outcomes of this approach have proven the validity of the method.]]></description>
      <pubDate>Fri, 29 Mar 2024 16:58:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2347365</guid>
    </item>
    <item>
      <title>Improved Signalized Intersection Performance using Computer Vision and Artificial Intelligence</title>
      <link>https://trid.trb.org/View/2319944</link>
      <description><![CDATA[The primary objectives of this research are to: (1) assess the feasibility and accuracy of using computer vision technology for performance evaluation at signalized intersections; (2) provide intersection video footage data captured by drones; (3) use computer vision and artificial intelligence to automatically convert data from video recordings at selected intersections into trajectories of road users; (4) using computer vision and artificial intelligence to count road users, and detect queuing and demand for each approach at selected intersections using drone footage; and (5) develop tools to facilitate Louisiana Department of Transportation (DOTD) traffic engineers in understanding road users' behaviors, evaluating intersection performance measures, and assisting in determining effective measures for improving safety and efficiency at intersections.
]]></description>
      <pubDate>Tue, 09 Jan 2024 10:28:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2319944</guid>
    </item>
    <item>
      <title>Data Fusion for Signalized Arterial Performance Measurement</title>
      <link>https://trid.trb.org/View/2308286</link>
      <description><![CDATA[This project focused on three applications of multiple data sources, or data fusion, to evaluate traffic operations on signalized arterials. The first application used a data fusion framework to evaluate its utility in predicting travel times on signalized arterials. The second application developed a framework that combined/enhanced the Automated Traffic Signal Performance Measures (ATSPM) generated through data fusion. The third project examined the use of probe vehicle data fusion for obtaining link speeds at signalized arterials by exploring the effects of market penetration rate (MPR), broadcasting frequency (BC), and aggregation level on the accuracy of the reported speeds. Results underscore the importance of BC, MPR, and aggregation levels. Importantly, varying the performance measures tended to yield different requirements in terms of probe MPR and BC.]]></description>
      <pubDate>Wed, 27 Dec 2023 10:29:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2308286</guid>
    </item>
    <item>
      <title>A Machine Learning Method for Saturation Headway Estimation Based on Commonly Available Information</title>
      <link>https://trid.trb.org/View/2237616</link>
      <description><![CDATA[Estimation of saturation headway at signalized intersections is of vital importance for traffic supply measurement and signal timing optimization. Most previous studies either need manual participation in collecting label values or require dynamic or aggregated data (e.g., heavy vehicle and turning ratio) to build estimation model. In this paper, the authors proposed a machine learning method for saturation headway estimation using commonly available features. To efficiently extract reliable saturation headway for model training, an aggregate-based signal cycle identification method was developed to obtain saturation headway sequence automatically by the designed rule. The results show the extreme values at the interior and the end of the continuous headway sequence are identified accurately. The significance of selected features is proved by KS test. Meanwhile, the performance in diverse cases validates the effectiveness and robustness of the proposed model.]]></description>
      <pubDate>Tue, 26 Sep 2023 16:04:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237616</guid>
    </item>
    <item>
      <title>Network-Wide Implementation of Roundabouts Versus Signalized Intersections on Urban Streets: Analytical and Simulation Comparison</title>
      <link>https://trid.trb.org/View/2237213</link>
      <description><![CDATA[This paper examines the impact of roundabouts implemented at intersections throughout a dense urban network on its operational performance. Metrics considered include the average free-flow speed, flow-moving capacity, trip-serving capacity, and fuel consumption rate. Three intersection strategies are compared: signalized intersections allowing left turns in a permitted manner (TWs), signalized intersections prohibiting left turns (TWLs), and modern roundabouts (RBs). Using the approaches of macroscopic fundamental diagrams and network exit functions, both analytical investigations and microscopic traffic simulations for grid networks were conducted. In general, the results from both analyses agree well. The results reveal that when single-lane roundabouts are applied in networks with a single travel in each direction, the RB network outperforms the TW network for all operational metrics. The RB network also outperforms the TWL network in free-flow speed and flow-moving capacity and has a similar trip-serving capacity as the TWL network. However, when roundabouts with two travel lanes are applied on multi-lane networks, the TWL network exceeds the RB network in both flow-moving and trip-serving capacities. This decrease in the performance of the RB network could possibly come from the complexity imposed on the entering vehicle that wants to use the inner lane. Moreover, because vehicles in the RB network need to accelerate/decelerate more frequently those in the other networks, the RB network generates a higher fuel consumption rate in uncongested and capacity conditions. The findings suggest intersections of roundabouts could be beneficial for networks with a single travel lane in each direction.]]></description>
      <pubDate>Thu, 31 Aug 2023 09:39:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237213</guid>
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