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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
    <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>Control Parameter Design for Adaptive Traffic Control System under Varying Traffic Conditions</title>
      <link>https://trid.trb.org/View/2646064</link>
      <description><![CDATA[This study establishes control parameter settings to enhance the performance of the adaptive traffic control system (ATCS) under varying traffic conditions. While ATCSs have been widely deployed in urban networks, the effect of different parameter values on ATCS performance remains largely unexplored. This study focuses on green time and cycle length control parameters, which determine the extent of signal timing adjustments in response to traffic fluctuations. These control parameters are examined based on the control algorithm for delay reduction (CAERUS), South Korea’s ATCS. CAERUS adjusts the green time and cycle length to minimize delays by equalizing the degree of saturation across movements within a target range of 0.80–0.95. The key traffic conditions influencing control parameter values are defined: (1) saturation deviation between conflicting movements for the green time control parameter; and (2) fluctuations in critical-lane group volume for the cycle length control parameter. A numerical simulation was conducted at three signalized intersections in Seoul. Results indicate that larger green time control parameter values effectively reduce saturation imbalances but may excessively shorten the green time of the uncongested movement, worsening its performance. Similarly, the cycle length control parameter value needs to be set according to critical-lane group fluctuations to prevent green time shortages and unnecessary delays. By establishing efficient control parameter settings under different traffic conditions, this study supports ATCS operational efficiency and smooth urban traffic flow.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646064</guid>
    </item>
    <item>
      <title>Behavioural modelling of the effects of countdown timers on rear-end collisions in the signal transition phase</title>
      <link>https://trid.trb.org/View/2627437</link>
      <description><![CDATA[Countdown timers at traffic signals display the remaining time for a phase to end and, in turn, influence driver anticipation, decision-making, and crossing behavior. They are used in many countries, often without proper guidelines. From a traffic psychology perspective, countdown timers can induce anticipation of signal change and reduce indecision during the signal transition phase, when the signal changes from green to yellow and then to red. From a driver behavior perspective, they can promote earlier braking, lower deceleration rates, and smoother maneuvers, which may affect the probability of rear-end collisions. However, the literature reports inconclusive findings on how countdown timers influence such collisions, and few studies have modelled this probability directly. This study analyses and models the impact of countdown timers on rear-end collisions during the signal transition phase. A surrogate safety measure of modified time to collision was used to identify potential rear-end conflicts. The temporal and spatial distribution of these conflicts was examined, and their occurrence was modelled statistically using vehicle trajectory data from urban intersections. Results show that countdown timers caused temporal and spatial variations in conflict distribution and were associated with a reduced probability of rear-end collisions, indicating safety benefits linked to driver behavioral changes.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:57:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627437</guid>
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    <item>
      <title>Guidebook on Determining Yellow and Red Intervals to Improve Signal Timing Plans for Left-Turn Movements</title>
      <link>https://trid.trb.org/View/2582254</link>
      <description><![CDATA[The objective of this research is to develop and test a comprehensive framework for setting yellow change and red clearance intervals for the left-turn movement, which can be used directly by the field traffic engineers in Texas. This guidebook provides a general description of the procedures entailed in this framework.]]></description>
      <pubDate>Sat, 22 Nov 2025 17:17:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582254</guid>
    </item>
    <item>
      <title>Explicit Coordinated Signal Control Using Soft Actor–Critic for Cycle Length Determination</title>
      <link>https://trid.trb.org/View/2448764</link>
      <description><![CDATA[Explicit signal coordination carries prior knowledge of traffic engineering and is widely accepted for global implementation. With the recent popularity of reinforcement learning, numerous researchers have turned to implicit signal coordination. However, these methods inevitably require learning coordination from scratch. To maximize the use of prior knowledge, this study proposes an explicit coordinated signal control (ECSC) method using a soft actor-critic for cycle length determination. This method can fundamentally solve the challenges encountered by traditional methods in determining the cycle length. Soft actor-critic was selected among various reinforcement learning methods. A single agent was administered to the arterials. An action is defined as the selection of a cycle length from among the candidates. The state is represented as a feature vector, including the cycle length and features of each leg at every intersection. The reward is defined as departures that indirectly minimize system vehicle delays. Simulation results indicate that ECSC significantly outperforms the baseline methods, as evident in system vehicle delay across nearly all demand scenarios and throughput in high demand scenarios. The ECSC revitalizes explicit signal coordination and introduces new perspectives on the application of reinforcement learning methods in signal coordination.]]></description>
      <pubDate>Fri, 31 Oct 2025 09:48:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2448764</guid>
    </item>
    <item>
      <title>Impact of automatic emergency braking on the benefits of red-light safety cameras</title>
      <link>https://trid.trb.org/View/2604659</link>
      <description><![CDATA[With the increasing prevalence of crash avoidance technologies, well-established effects of traditional safety countermeasures may change. This study examines the impact of automatic emergency braking (AEB) systems on the effects of red-light safety cameras on real-world crashes at signalized intersections. Effects of AEB systems alone on rear-end crashes and rear-end injury crashes at signalized intersections were estimated using crash data from 19 states and Poisson regression models. Effects of red-light safety cameras alone on rear-end crashes, rear-end injury crashes, all crashes, and all injury crashes were evaluated by meta-analysis of 35 previous studies. Based on these two analyses, effects of red-light safety cameras on crashes were calculated assuming that 28%, 51%, 95%, and 100% of vehicles on the road were equipped with AEB systems. As the percentage of vehicles equipped with AEB grows, the increases in rear-end crashes and rear-end injury crashes associated with red-light safety cameras decline, and the reductions in all crashes and all injury crashes grow. With no AEB impact, red-light safety cameras are associated with a 21% increase in rear-end crashes, a 10% increase in rear-end injury crashes, a 7% reduction in all crashes, and a 19% reduction in all injury crashes. Those figures improve in tandem as more vehicles are equipped with AEB. Assuming all vehicles have AEB, the increases in rear-end crashes and rear-end injury crashes decline to 13.5% and 5.9%, respectively, and the reductions in all crashes and all injury crashes grow to 9.7% and 20.2%, accordingly. The growing prevalence of AEB systems will extend the benefits of red-light safety cameras by mitigating associated increases in rear-end crashes. Estimates of the benefits of other proven safety countermeasures should also be reevaluated to account for the growing impact of crash avoidance technologies.]]></description>
      <pubDate>Mon, 27 Oct 2025 09:34:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604659</guid>
    </item>
    <item>
      <title>How and why do human factors impact cyberattack consequences at a connected intersection? − A falsified red light countdown case study</title>
      <link>https://trid.trb.org/View/2600613</link>
      <description><![CDATA[Vehicle-to-infrastructure communication enhances traffic safety but is vulnerable to cyberattacks. Spoofing cyberattacks pose data falsification that endangers road users. Given that achieving high-level (L4-L5) automated driving still requires significant progress, understanding how and why human factors influence cyberattack consequences is novel yet essential for mitigating these risks. Research on this topic remains limited, and collecting driving behavior data under cyberattack conditions is challenging due to safety concerns. To address this, the authors conducted a driving simulator experiment with 32 drivers spanning a range of experience levels and other factors, replicating a connected intersection under no-attack and cyberattack scenarios. In-vehicle falsified red-light countdown spoofing attacks are designed to provide false information on the dashboard. Using the Surrogate Safety Assessment Model, safety consequences were measured. Results indicate that cyberattacks pose significant threats to traffic safety. Greater speed at the end of the countdown period increases the risk of frontal (pedestrian and right-angle) collisions and reduces rear-end collision risks. Experienced drivers show lower hazards for frontal collisions. Notably, the total hazards under cyberattacks do not differ significantly between male and female drivers. Human factors affect safety by influencing driving behavior. Experienced drivers decelerate over shorter distances, reducing collision risk, while male and female drivers show similar deceleration patterns, resulting in comparable safety consequences. These findings provide a quantitative model describing human factors impacting cyberattack consequences, inform safer transportation management, and, more importantly, educate the public about cyberattacks. Future models can be developed to predict collision probabilities and improve system resilience (i.e., recovery after cyberattack).]]></description>
      <pubDate>Mon, 13 Oct 2025 08:48:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2600613</guid>
    </item>
    <item>
      <title>Yellow and Red Intervals to Improve Signal Timing Plans for Left-Turn Movements</title>
      <link>https://trid.trb.org/View/2570926</link>
      <description><![CDATA[This project intends to develop and test a framework for setting yellow change and red clearance intervals for the left-turn movement, which can be used directly by the field traffic engineers in selecting the appropriate values for parameters in the proposed framework. The framework is designed to incorporate a comprehensive set of parameters related to intersection geometry, perception, human comfort, driver's behavior, safety issues, and traffic related laws. The application of this proposed framework is expected to improve both the left-turn movement safety and the efficiency at the intersection. This is the interim report for the project, which summarizes the work that has been performed during the first year (2001-2002) of this two-year project.]]></description>
      <pubDate>Mon, 01 Sep 2025 16:31:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570926</guid>
    </item>
    <item>
      <title>Engineering countermeasures for red-light running: a state-of-the-art review</title>
      <link>https://trid.trb.org/View/2563050</link>
      <description><![CDATA[]]></description>
      <pubDate>Tue, 10 Jun 2025 14:47:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563050</guid>
    </item>
    <item>
      <title>Evaluation of Intersection Safety and Capacity Relevant to Signal Timing on Arizona’s State Highway System</title>
      <link>https://trid.trb.org/View/2550937</link>
      <description><![CDATA[Traffic signal phase-change intervals are intended to provide a safe transition between two conflicting signal phases or a right-of-way transition between conflicting road-user movements. As a result, signal phase change (includes yellow change interval, red-clearance interval, and pedestrian intervals) has significant safety and operations implications at signalized intersections. Currently, there is no national standard for calculating these durations. Additionally, the current ADOT-recommended practice for calculating traffic signal intervals has resulted in two issues: (1) lengthy red-clearance intervals at interchanges with large conflict areas; and (2) yellow change interval durations with the potential of not meeting the needs of the driver population. The objectives of this research were to: (1) evaluate ADOT’s current signal timing design guidelines; and (2) recommend an optimal signal timing design for the Arizona state highway system. A comprehensive evaluation of the effects of the current signal timing design guidelines and a thorough pilot study were used to inform a set of recommendations. These recommendations were used to draft a proposed version of the ADOT’s Guidelines and Processes, which describes the interval duration calculation methods. Proposed changes to the methods include (1) an increase in the approach speed used to calculate left-turn yellow-change intervals; (2) an increase in the intersection speed used to calculate the red-clearance intervals at single-point urban interchanges; and (3) a decrease in the walk speed used to compute “DON’T WALK” interval durations at locations with a high volume of slower-moving pedestrians. These changes would yield increased yellow-change intervals, decreased red-clearance intervals, and longer “DON’T WALK” intervals.]]></description>
      <pubDate>Tue, 27 May 2025 09:33:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550937</guid>
    </item>
    <item>
      <title>Impact of Extended Red Clearance Intervals on Left-Turn Red-Light Violations: A Time Series Analysis</title>
      <link>https://trid.trb.org/View/2556778</link>
      <description><![CDATA[Red-light running (RLR) incidents at signalized intersections contribute substantially to intersection-related crashes. They are increasing in number, posing a significant safety challenge. This study examines the impact of signal timing parameters, specifically red clearance intervals, on left-turn RLR violations and assesses effective countermeasures. Adjustments to red clearance and yellow intervals, in line with the ITE 2020 guidelines, were made at signalized intersections in major urban areas within the City of Phoenix. The measurements for left-turn widths at the selected study sites ranged from 60 to 125?ft, with ITE calculations resulting in red clearance intervals between 3 and 5?s. Smart sensors were deployed to collect comprehensive data on signal timings, traffic counts, and RLR violations before and after implementing the updated intervals. Analysis of the pre-and post-intervention data revealed a striking 112% increase in the frequency of RLR violations for left-turn movements following the extension of red clearance intervals. Additionally, the synergistic impact of red clearance and yellow intervals showed that the negative impact of the increased red clearance intervals had offset the positive effect of the extended yellow interval. Further analysis using the interrupted time series method confirmed a significant rise in RLR violations associated with extended red clearance intervals. These findings suggest that increasing the duration of red clearance intervals significantly increases the frequency of RLR violations for left-turn movements. While the study is focused on the City of Phoenix, its implications are relevant to urban areas with similar traffic characteristics. This underscores the importance of thoughtful consideration when modifying signal timings to address RLR behavior effectively.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2556778</guid>
    </item>
    <item>
      <title>An experimental study on Max-Pressure traffic controller based on travel time delays</title>
      <link>https://trid.trb.org/View/2534363</link>
      <description><![CDATA[First, this paper develops and examines a new travel time-based traffic signal controller. By applying a decentralized approach that relies solely on local information, the controller optimizes phase duration to maximize throughput. Three issues from previous queue-based max-pressure controllers are addressed: (i) infinite link capacity, (ii) non-fixed phase sequences, and (iii) the neglect of spatial distribution of the queues. The newly developed cyclic time-based max-pressure control is compared with existing cyclic queue-based and time-based max-pressure schemes. The results show that the introduced time-based approach offers improved fairness and more effective decision-making.Second, new time-varying schemes are developed for the time-based max-pressure controller: bounded cycle lengths, which impose stability-preserving minimum and maximum limits, and unbounded cycle lengths, which dynamically adjust based on real-time traffic flow for enhanced flexibility. A comparative analysis with fixed cycle length schemes highlights the advantages of appropriate cycle length allocation for improved performance.Finally, the developed controller was evaluated through simulation tests and an experimental case study in an urban traffic network, specifically at a complex T-intersection. The intersection, which was originally controlled by loop detectors, was isolated from adjacent intersections for the experiment, with the loop detectors disconnected. Traffic control was instead managed using Bluetooth data, providing real-time travel time information and enabling the intersection to adapt to current traffic conditions while complying with traffic regulations and constraints. The results demonstrated that, despite the constraints, the adaptive controller performed more effectively than the actuated controller, providing a cost-effective solution for traffic control.]]></description>
      <pubDate>Tue, 20 May 2025 11:38:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534363</guid>
    </item>
    <item>
      <title>Traffic Signal Cycle Control With Centralized Critic and Decentralized Actors Under Varying Intervention Frequencies</title>
      <link>https://trid.trb.org/View/2487937</link>
      <description><![CDATA[Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal control (TSC) is a key strategy for reducing congestion. Unlike most TSC systems that rely on high-frequency control, this study introduces an innovative joint phase traffic signal cycle control method that operates effectively with varying control intervals. The authors' method features an adjust all phases action design, enabling simultaneous phase changes within the signal cycle, which fosters both immediate stability and sustained TSC effectiveness, especially at lower frequencies. The approach also integrates decentralized actors to handle the complexity of the action space, with a centralized critic to ensure coordinated phase adjusting. Extensive testing on both synthetic and real-world data across different intersection types and signal setups shows that their method significantly outperforms other popular techniques, particularly at high control intervals. Case studies of policies derived from traffic data further illustrate the robustness and reliability of their proposed method.]]></description>
      <pubDate>Mon, 05 May 2025 09:08:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487937</guid>
    </item>
    <item>
      <title>Effect of a Novel Framework for Transit Signal Priority Systems on Urban Networks: A Real-World Case Study</title>
      <link>https://trid.trb.org/View/2529579</link>
      <description><![CDATA[Transit signal priority (TSP) is a crucial tool for enhancing the efficiency of urban public transportation. However, implementing control algorithms within a TSP system presents several challenges, including data transmission, the stochastic nature of traffic, and real-time operation. To address these issues, this study proposes a TSP control framework that leverages the strengths of both single-intersection and multi-intersection control to minimize red light waiting times. The framework is structured into three tiers: the cloud, the roadside unit (RSU), and the onboard unit (OBU). Each of these is responsible for implementing control strategies and providing driving speed advisories. By accounting for the differences between simulation and real-world systems, this framework ensures rational traffic control, enabling buses to operate more efficiently while minimizing the negative impact of side-street signals on the overall road network.]]></description>
      <pubDate>Thu, 27 Mar 2025 11:35:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2529579</guid>
    </item>
    <item>
      <title>Using Time Signal at Red (TSAR) as a tool for analysing rail network performance</title>
      <link>https://trid.trb.org/View/2499507</link>
      <description><![CDATA[Reactionary delays can adversely impact train service performance. This is particularly true for parts of the rail network at or near capacity. To detect the causes of such delays, a metric with a granularity smaller than those of typical rail delay metrics is required. The authors present an approach based on the Time Signal at Red (TSAR) metric. The purpose of TSAR is to measure the duration a berth is continuously occupied by a train or reserved, which is closely related to information regarding the red aspect of the berth signal at an entrance to the berth. Thus, TSAR provides a low-level metric to measure individual service and berth performance, and to observe system effects that reflect reactionary delay. The paper defines TSAR and describes a data processing methodology to extract TSAR and signal aspect on berth entry from disparate data sources. The use of TSAR is demonstrated for a case study area – comparing different service patterns, identifying patterns of reactionary delay, and showing the impact of adhesion at different times of year. The implications of TSAR are discussed, including its utility for applications such as analysis of simulated network performance.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2499507</guid>
    </item>
    <item>
      <title>Estimating crash reductions at signalised intersections in connected-vehicle environment</title>
      <link>https://trid.trb.org/View/2441596</link>
      <description><![CDATA[Crashes at signalised intersections caused by drivers disobeying red light signals increase the risk of serious injuries or fatalities. A Field Operational Test (FOT), known as the Ipswich Connected Vehicle Pilot (ICVP), sought to understand the capability and impact of connected vehicle technology including the estimated crash reductions of selected safety use cases. A methodology was proposed for estimating the crash reduction for the Advanced Red-Light Warning (ARLW) use case. The methodology was based on a well-known power model which assumes that changes in road trauma can be explained using the change in mean travel speed. This paper outlines the results of an application of this methodology to the FOT data collected over approximately 12 months. The results indicated potential reductions of 9.72%, 5.85% and 3.98% in fatal crashes, serious injury crashes, and slight injury crashes, respectively.]]></description>
      <pubDate>Tue, 15 Oct 2024 13:33:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2441596</guid>
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