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    <title>Transport Research International Documentation (TRID)</title>
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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>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Improving Urban Road Safety: A Data-Driven Approach combining Analytical and Simulation Models for Effective Traffic Calming Interventions</title>
      <link>https://trid.trb.org/View/2682051</link>
      <description><![CDATA[Road safety in urban areas is a key challenge for sustainable mobility and quality of life. Pedestrians are vulnerable to risks from both vehicle traffic and unsafe behaviour. The use of smartphones, whether while driving or crossing the road, has introduced new forms of distraction that impair attention and increase the risk of collisions. In addition, non-compliance with traffic rules, such as speeding limits and running red lights, contributes to an unsafe urban environment. Advanced Traffic Management Systems (ATMS), including Red-Light Enforcement (RLE) and Automated Speed Enforcement (ASE) systems, aim to reduce collisions by encouraging safer driving. However, limited resources make it essential to strategically place these systems for maximum effectiveness. This study presents a data-driven approach that integrates real-world data with analytical and simulation models to assess the probability of collisions and analyze the effects of the placement of these systems. The proposed methodology allows to evaluate traffic calming measures and their impact. Using fixed sensors to collect vehicle speeds and gap distances, we performed statistical analyses to quantify speed reductions and assess the risks for pedestrian safety by calculating the Collision Probability (CP) using a calibrated microsimulation model. The methodology has been tested in Catania (Italy), where recent investments have enabled the installation of these AMTS and RLE systems. The results demonstrate a significant reduction in CP: from 10.24% to 7.78% in the 5:00–6:00 time slot and from 36.89% to 27.86% in the 8:00–9:00 slot. This corresponds to a relative decrease in CP of 24% and 25%, respectively. These findings highlight the potential of the proposed tool to support administrations in evaluating traffic interventions, with scalable applicability to similar urban contexts and tangible benefits for both policymaking and pedestrian safety.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682051</guid>
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    <item>
      <title>A bibliometric performance and network analysis of red-light camera impact on signalized intersection safety</title>
      <link>https://trid.trb.org/View/2673094</link>
      <description><![CDATA[Red-light running (RLR) at signalized intersections poses a significant threat to traffic safety, contributing to thousands of fatalities and injuries globally each year. Despite the introduction of red-light cameras (RLCs) to mitigate these violations, their effectiveness remains contentious, with inconsistent results across studies. This article utilizes bibliometric performance and network analysis (BPNA) alongside a systematic literature review (SLR) to evaluate the impact of RLCs on signalized intersection safety.   By analyzing literature from 1975 to 2024, the study reveals a complex landscape of research themes, highlighting both the successes and challenges associated with RLC implementation.   The findings identified prominent research themes, influential authors, institutions, and gaps in the literature, emphasizing the need for comprehensive strategies that combine RLC implementation with community engagement and driver education to enhance traffic safety. It also illustrates the necessity for continued exploration of RLC efficacy and the development of innovative approaches to enhance compliance, ultimately informing policymakers and traffic safety advocates about the critical importance of addressing RLR in urban areas. The study advocates for the integration of RLC systems with emerging technologies, such as vehicle-to-infrastructure (V2I) communication. As connected vehicles become increasingly prevalent, the synergy between RLCs and V2I systems could facilitate a more comprehensive understanding of traffic environments, thus enabling real-time updates and enhanced driver assistance functionalities for improving traffic safety and efficiency at signalized intersections.]]></description>
      <pubDate>Tue, 07 Apr 2026 15:36:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673094</guid>
    </item>
    <item>
      <title>Traffic Prediction-Based Individualized Driver Warning System to Reduce Red Light Violations</title>
      <link>https://trid.trb.org/View/2645442</link>
      <description><![CDATA[Running red lights remains a major cause of intersection crashes, injuries, and fatalities. Although numerous counter-measures have been proposed, they continue to be a major problem in practice, partly because most existing systems deliver uniform guidance to every driver, prompting some motorists to ignore or misinterpret the alerts and leaving a persistent safety gap. We present a novel method for providing accurate, individualized warnings in place of the broad, one-size-fits-all alerts used by most existing systems. Recognizing if a driver will run red lights is highly dependent on signal phase and timing, traffic conditions along the road, and individual driver behavior; the proposed warning system contains three parts. First, a traffic prediction algorithm uses vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) data to predict traffic conditions along the approach to a signalized intersection. Second, an optimization-based algorithm that computes driver-specific warning that minimizes the risk of a red light violation, given the predicted traffic states and driver reaction model. Third, the resulting advisory—quantifying the required deceleration—is presented on the in-vehicle display and updated continuously as the vehicle approaches the intersection. Both numerical simulated driving scenarios and real-world road tests are used to demonstrate the proposed algorithm’s performance under different conditions by comparing with previous work on the red light running warning system (RLRWS). The results show that the proposed system provides more effective and accurate warning signals to drivers. In the simulation, the proposed algorithm cuts the ego vehicle’s peak deceleration by up to 72.2% relative to an unguided baseline, greatly reducing the risk of red light violation.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645442</guid>
    </item>
    <item>
      <title>Investigation of e-Bike Red-Light Running Heterogeneity with Intelligent Intersection Sensing Data</title>
      <link>https://trid.trb.org/View/2613349</link>
      <description><![CDATA[Red-light running (RLR) behavior by e-bikes significantly increases the risk of traffic accidents, endangering the safety of e-bike riders, vehicle drivers, and pedestrians. This study explores the heterogeneity in RLR behavior among different e-bike rider groups at urban intersections using data collected from intelligent sensing systems in Beijing. The research integrates continuous trajectory data with signal phase, vehicle presence, and non-motorized vehicle/pedestrian information to assess the factors influencing RLR behavior. A Multinomial Logit (MNL) model was employed to analyze and compare the RLR tendencies of regular riders, delivery riders, and couriers. The findings highlight that delivery riders are more likely to engage in RLR due to time constraints, whereas higher densities of non-motorized vehicles and pedestrians reduce the likelihood of such behavior across all rider types, likely due to increased caution. The presence and direction of vehicles within intersections were shown to play a critical role in shaping RLR decisions. These results provide valuable insights into violation patterns and inform targeted traffic management strategies aimed at improving road safety and reducing accidents involving e-bike riders.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613349</guid>
    </item>
    <item>
      <title>Improving traffic signal data quality for the Waymo open motion dataset</title>
      <link>https://trid.trb.org/View/2636264</link>
      <description><![CDATA[Datasets pertaining to autonomous vehicles (AVs) hold significant promise for a range of research fields, including artificial intelligence (AI), autonomous driving, and transportation engineering. Nonetheless, these datasets often encounter challenges related to the states of traffic signals, such as missing or inaccurate data. Such issues can compromise the reliability of the datasets and adversely affect the performance of models developed using them. This research introduces a fully automated approach designed to tackle these issues by utilizing available vehicle trajectory data alongside knowledge from the transportation domain to effectively impute and rectify traffic signal information within the Waymo Open Motion Dataset (WOMD). The proposed method is robust and flexible, capable of handling diverse intersection geometries and traffic signal configurations in real-world scenarios. Comprehensive validations have been conducted on the entire WOMD, focusing on over 360,000 relevant scenarios involving traffic signals, out of a total of 530,000 real-world driving scenarios. In the original dataset, 71.7 % traffic signal states are either missing or unknown, all of which were successfully imputed by our proposed method. Furthermore, in the absence of ground-truth signal states, the accuracy of our approach is evaluated based on the rate of red-light violations among vehicle trajectories. Results show that our method reduces the estimated red-light running rate from 15.7 % in the original data to 2.9 %, thereby demonstrating its efficacy in rectifying data inaccuracies. This paper significantly enhances the quality of AV datasets, contributing to the wider AI and AV research communities and benefiting various downstream applications. The code and improved traffic signal data are open-sourced at: https://github.com/michigan-traffic-lab/WOMD-Traffic-Signal-Data-Improvement.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636264</guid>
    </item>
    <item>
      <title>Special Crash Investigations: On-Site Rollover Crash Investigation; Vehicle: 2018 Jeep Compass; Location: California; Crash Date: January 2022</title>
      <link>https://trid.trb.org/View/2672497</link>
      <description><![CDATA[This Special Crash Investigations report documents the on-site investigation of a two-vehicle crash and rollover of a  stolen 2018 Jeep Compass in California in January 2022. The Jeep was driven by an unbelted 32-year-old male with  a front-right seat passenger, an unbelted 25-year-old female. The other vehicle was a 2019 Lexus ES300H driven by  a belted 61-year-old male with a front-right seat female passenger, age unknown. According to the police crash  report, the pre-dawn crash occurred in clear weather when the Jeep failed to stop at a red light, entered the  intersection, and was T-boned on the right side by the Lexus. The Jeep rotated clockwise, tripped left side leading,  rolled four or five times, and landed in the west leg of the intersection on its roof. The Jeep driver fled the scene on  foot but was later arrested by a separate police agency. The Jeep passenger had police-reported “A” (severe) injuries  including skull and facial fractures. She was transported by ambulance to a local trauma center and her treatment  status is unknown. The Lexus driver complained of lower back and knee pain. He was transported by ambulance to a  local hospital but his treatment status is unknown.]]></description>
      <pubDate>Wed, 25 Feb 2026 09:33:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672497</guid>
    </item>
    <item>
      <title>Platoon and Red Light Violation Detection Using Image Processing</title>
      <link>https://trid.trb.org/View/2113532</link>
      <description><![CDATA[Intersections account for most road accidents and delay in a road network. The intersection's efficiency and safety concerns can be addressed by collecting vehicle platoon size, queue length, and delay data and implementing a red light violation detection technique (RLVDS) to reduce accidents. It is challenging to collect this traffic information in heterogeneous and less lane disciplined traffic, a scenario often observed in developing countries such as India. Traditional sensors such as inductive loop, infrared, radar, or magnetic sensors and image processing solutions do not work well under these conditions. Hence the current work presents a set of robust techniques developed for heterogeneous traffic. First, the platoons were detected using foreground extraction, connected component analysis, and a density-based clustering algorithm. Then, the queue length was extracted using a progressive block processing technique. Separately the RLVD was performed using corner point tracking and user-defined detection zones.]]></description>
      <pubDate>Tue, 24 Feb 2026 08:30:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113532</guid>
    </item>
    <item>
      <title>Identification of factors influencing pedestrians' red-light crossing behavior based on interpretable machine learning</title>
      <link>https://trid.trb.org/View/2647872</link>
      <description><![CDATA[Pedestrians' crossing the street while running a red light are a significant factor contributing to traffic accidents at intersections. Traditional models fail to capture the complex, multifactorial nonlinearities and interactions involved in this behavior due to their limited linear analytical power, while machine learning models suffer from interpretability issues. To address this, an analytical framework that combines data-driven machine learning algorithms with emerging interpretability techniques was proposed, aiming to reveal the complex, nonlinear effects and relative importance of factors influencing pedestrian red-light-crossing behavior. Empirical video data from five signalized intersections in Hefei, China were used to compare the modeling and prediction performance of four methods: logistic regression, K-nearest neighbors, support vector machine, and extreme gradient boosting (XGBoost). Shapley Additive Explanations (SHAP) and Accumulated Local Effects (ALE) were employed to evaluate the key factors influencing pedestrians' decisions to cross at a red light. The results show that the XGBoost model outperforms the other algorithms in capturing the complex relationships among influencing factors and accurately identifying red-light-running behavior. Quantitative analysis of feature importance reveals that traffic volume is the most influential predictor, followed by pedestrian walking speed, red-light duration, conformity behavior, and age. This study overcomes the linear constraints of traditional regression models and provides a theoretical foundation for optimizing traffic management and developing intelligent law enforcement strategies.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:02:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647872</guid>
    </item>
    <item>
      <title>Identifying Near-Misses (Including Red-Light Running) and Reducing Conflict Through D-FYA at Signalized Intersections Using LiDAR Sensors</title>
      <link>https://trid.trb.org/View/2646974</link>
      <description><![CDATA[The primary research objective of this project is to demonstrate the potential capability of emerging LiDAR (Light Detection and Ranging) sensing technologies in identifying and mitigating traffic conflicts (i.e., near-misses) at signalized intersections. The latest LiDAR sensing technologies allow for tracking vehicles and pedestrians (assigning each a unique temporary ID for identification). This capability can be used to mitigate traffic crashes. In this research, the collaborating team between the University of Utah, the University of Texas at Arlington (UTA), and the University of Maryland installed the application software developed by UTA and conducted extensive tests in the field, such as the sensors’ latency to identify instantaneous traffic conflicts, the algorithm’s reliability and accuracy, and a demonstration of safety-centric traffic control algorithms The outcome of this research is expected to provide decision support for Utah Department of Transportation (UDOT) to determine its large-scale deployment plan.]]></description>
      <pubDate>Thu, 22 Jan 2026 09:10:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646974</guid>
    </item>
    <item>
      <title>Time series analysis of resampled red-light violations to understand drivers’ adaptation to extended yellow intervals</title>
      <link>https://trid.trb.org/View/2630692</link>
      <description><![CDATA[Red-light running (RLR) poses significant safety risks at signalized intersections, often leading to severe crashes. Increasing yellow intervals has been proposed as a countermeasure for RLR violations. Despite the immediate effectiveness of increasing yellow intervals in reducing RLR violations, concerns remain regarding potential driver adaptation over time. This study aimed to evaluate the long-term effectiveness of increased yellow intervals on driver compliance using RLR events on through and left-turn movements.   The data were collected from three intersections in the Phoenix metropolitan area for 81 and 496 days (about one and a half years) before and after increasing the through and left-turn yellow intervals. An Interrupted Time Series Analysis (ITSA) and Block Bootstrap Resampling were applied to determine the significance of the intervention and the impact of the intervention through time.   The results showed that increasing the yellow intervals led to a significant and sustained reduction in RLR violations for both through and left-turn movements across all treatment sites. Importantly, ITSA results indicated no evidence of driver adaptation, reinforcing the long-term effectiveness of increasing yellow intervals on RLR. This research also shows the impact of movement types and site-specific characteristics, including traffic volume and intersection layout, on the effectiveness of signal timing adjustments for safety improvements.   Understanding the drivers’ adaptation to the changes in signal timing could assist agencies when considering using longer yellow intervals to address chronic red light running violations and defining the allowable period before readjustment of signal timing plans for safety purposes.]]></description>
      <pubDate>Tue, 23 Dec 2025 09:29:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630692</guid>
    </item>
    <item>
      <title>Future-Ready Delaware: SMART Technology Enhancements for Safe, Resilient Intersections: Implementation Report</title>
      <link>https://trid.trb.org/View/2607949</link>
      <description><![CDATA[The rapid advancement of digital technologies, big data analytics, and cellular communications is transforming the way transportation systems operate. These innovations present a unique opportunity to address longstanding challenges in mobility and safety. This project, led by the Delaware Department of Transportation (DelDOT), explores the potential of connected vehicle and vehicle-to-everything (V2X) technologies to enhance transportation safety and efficiency. Specifically, it evaluates the viability and scalability of Network V2X system architectures for low-latency applications that can reduce transportation-related injuries and fatalities and improve mobility for all users—both within Delaware and beyond.]]></description>
      <pubDate>Tue, 28 Oct 2025 16:54:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2607949</guid>
    </item>
    <item>
      <title>Motorcyclists’ Red-light Behavior at Signalized Intersections in the Accra Metropolis, Ghana</title>
      <link>https://trid.trb.org/View/2571458</link>
      <description><![CDATA[The increasing use of motorcycles in urban Africa is receiving attention in the research community. In this paper, the authors explore the nuanced characteristics of motorcycle use and behavior of riders in Accra, the capital of Ghana. Observational checklist was used to profile riders (and pillion passengers) and observe their everyday behaviors respectively at three purposively selected signalized intersections over a five-day period. A total of 4,895 motorcyclists were observed over the period. A statistically significant relationship was found between motorcyclists’ red-light behavior at signalized intersections, and the time of travel, day of observation, the presence of pillion passenger, the presence of other motorcyclists waiting at the intersection as well as rider’s use of helmet, sunshield, mobile phone, headphone. The study found high rate of red-light violations and the low-level safety consciousness among motorcyclists. These finding and other unobserved factors explain the high rate of crashes and fatalities among motorcyclists in Ghana. It is recommended for officials of the National Road Safety Authority and the Motor Transport and Traffic Department of the Ghana Police Service to increase their surveillance at the intersections to ward off recalcitrant motorcyclists.]]></description>
      <pubDate>Mon, 29 Sep 2025 08:35:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571458</guid>
    </item>
    <item>
      <title>Influence of Rainfall on the Probability of Red-Light Running at Signalised Intersections</title>
      <link>https://trid.trb.org/View/2406843</link>
      <description><![CDATA[Red-light running (RLR) occurs when drivers violate the red light either because they cannot stop or choose not to stop. The study aims to assess the influence of rainfall on red-light running at a signalized intersection. Traffic and rainfall data were collected at three selected sites in Durban, South Africa. Each site was divided into three zones (free flow, transition, and posted speed) and posted speed subdivided into a dilemma and braking sections). At selected 4-legged signalized intersections, traffic data were extracted from traffic videotapes obtained from eThekwini Transport Authority, Durban, and verified manually on-site. All selected four sites have posted speed limit of 60 km/h; two sites observed a cycle time of 100 s, and the other two sites 120 s. Traffic volume is converted to traffic flow with modified passenger car equivalent values. The modification is needed because of the effect of rainfall on the traffic stream. Rainfall intensity was classified according to the rate of precipitation as follows; rainfall intensity (i): light rain (i < 2.5 mm/h); Moderate rain (2.5 mm/h ≤ i < 10 mm/h), and heavy rain (10 ≤ i ≤ 50 mm/h) as prescribed by the World Meteorological Society. When it rains, drivers reduce vehicle speed and increase the follow-up gap because of poor visibility and poor road surface traction. Generally, rainfall caused an average speed reduction from 80 km/h during dry weather to 50 km/h (37.5%) during light rain; to an average speed of 40 km/h (50%) during moderate rain and an average speed of 33 km/h (58.7%) during heavy rain. The logistic regression model proved to be a good fit probabilistic method of predicting red-light running. The model was successful in predicting red light running during dry weather and rainfall conditions. Results show that rainfall hurts red-light running at signalized intersections. The paper concluded that the probability of red-light running during rainfall is significantly lower than in dry weather.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2406843</guid>
    </item>
    <item>
      <title>Evaluating the Effectiveness of Red-Light Running Cameras on Intersection Crash Outcomes and Professional Perspectives</title>
      <link>https://trid.trb.org/View/2592201</link>
      <description><![CDATA[Red-light running (RLR) stands out as a highly risky behavior, increasingly emerging as a leading contributor to intersections crashes, with red light cameras (RLCs) emerging as a prevalent enforcement tool. While numerous studies have analyzed the effectiveness of RLCs and factors influencing RLR, this study distinguishes itself by incorporating a comprehensive approach that includes crash data analysis, surveys, and discussions. This study examines the impact of RLCs on intersection safety, with a focus on analyzing crash data, professional opinions, and the overall effectiveness of such programs. Utilizing a comprehensive methodology that includes quantitative analysis, surveys, and discussions, the study evaluates the effectiveness of RLCs in reducing various types of crashes, including angle, left-turn, and rear-end collisions. Results show that RLCs significantly reduce the severity of crashes, particularly fatal and injury (FI)-related incidents, with a noted decrease in angle collisions and severity. While rear-end collisions increased during RLCs operation, the post-ban period exhibited a decrease in both rear-end and other collision types. Insights from professionals highlight the perceived safety benefits of RLCs, though challenges related to public perception, operational costs, and administrative processes persist. The findings provide valuable recommendations for optimizing RLCs programs and enhancing intersection safety through improved enforcement strategies and public engagement.]]></description>
      <pubDate>Tue, 26 Aug 2025 09:20:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592201</guid>
    </item>
    <item>
      <title>Predicting Red Light Running Violation Using Machine Learning Classifiers</title>
      <link>https://trid.trb.org/View/2407682</link>
      <description><![CDATA[Red light Running (RLR) violation remains as an important road safety concern at urban intersections. Existing method have mostly used statistical regression-based methods to explore factors contributing to RLR. However, it is well-known that statistical methods are based on predefined associations among variables and are unable to capture latent heterogeneity. This study aims to classify and predict RLR using spatial analysis and machine learning (ML) methods. Georeferenced RLR violation data for the year 2016 was collected for the city of Luzhou, China. To identify violation hotpots, frequency-based clustering was carried out using collect event tool in ArcMap geographic information system (GIS). Prior to RLR prediction via ML, data imbalance problem was addressed using a random over-sampling technique. Two widely used ML algorithms, i.e., Random Forest (RF) and Gradient Boosted Decision Tree (GBDT), were then used for prediction and classification of RLR. The performance of these models was assessed with accuracy and Cohen's kappa. The results showed that GBDT had an overall accuracy of 96% outperformed the RF.]]></description>
      <pubDate>Mon, 18 Aug 2025 08:51:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407682</guid>
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