<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <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" />
    <description></description>
    <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>
    </image>
    <item>
      <title>Dynamic dilemma zone at signalized intersection: Attention allocation patterns using cure survival analysis for male riders</title>
      <link>https://trid.trb.org/View/2655851</link>
      <description><![CDATA[The design of the signal at the intersection considers the constant speed of the riders and the dilemma zone to be static. However, these assumptions may not hold true in complex environments with multiple users. This study explores the dynamic dilemma zone by incorporating the time to detect the signal by analyzing the drivers’ eye gaze movements and attention allocation patterns. The delay in detecting the amber phase of the signal can put drivers in a situation where they can neither safely cross the intersection nor stop before the stop line. The experiments were conducted in a virtual environment with 105 participants predominantly considering male riders. The image processing algorithms identified the first instance of riders noticing the amber phase. The parametric cure survival models were used to quantify the time to detect the signal as they incorporate the fact that some drivers may not look at the signal for the entire duration. This study further considered the complex decision-making of speeding and decelerating at the onset of amber phase at signalized intersections. The riders’ choices to vary the speed and safely or unsafely crossing the signal were quantified across psychological constraints. The results revealed that the odds of unsafe crossing at signal increased by 3.3, even in situations where riders were talking to pillion riders. The results indicated that riders under time pressure were more focused on the road, and their time to detect the signal was 0.72 s more than the base conditions.]]></description>
      <pubDate>Wed, 04 Feb 2026 17:05:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655851</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>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>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>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>Dynamic Flashing Yellow Arrow Phase Mode Selection</title>
      <link>https://trid.trb.org/View/2533887</link>
      <description><![CDATA[This project focused on utilizing high-resolution signal data, crash data, and volume data to analyze safety impacts associated with the three left-turn phasing modes that flashing yellow arrows could operate in and determine when the various modes should operate. The three modes were as follows: Protected only; Protected-permissive; Permissive only. The project team needed to analyze the data in two different methods to better understand the results. The analysis was completed for 9 scenarios with different combinations of: Speed (Low/High); Lateral Offset (Positive/Negative); Left-Turn Lane (Single/Dual). The result of this project was a methodology that could be used for future analysis and incorporation of safety data into flashing yellow arrow (FYA) operations decisions. No specific updates were made to the existing FYA phase mode decision spreadsheet.]]></description>
      <pubDate>Thu, 10 Apr 2025 09:21:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2533887</guid>
    </item>
    <item>
      <title>Impact of countdown timer on drivers’ anticipation at the onset of yellow at signalized intersections</title>
      <link>https://trid.trb.org/View/2516994</link>
      <description><![CDATA[Countdown timers at traffic signals display the time remaining for a phase to terminate, influencing driver behaviour. Studies on their effects at the onset of yellow have generally focused on drivers’ stop/go decisions and dilemma zone, without explicitly addressing how they help drivers anticipate the signal change and make ‘informed decisions’. Therefore, this study first analysed the impact of countdown timer on drivers’ anticipation by comparing brake initiation, deceleration, and speed in the absence and presence of countdown timer. Next, driver behaviour was studied using ‘Accelerated Failure Time’ models. A driving simulator experiment was designed for data collection, with demographic and additional driving behaviour data gathered through a questionnaire. When countdown timer was present, drivers anticipated the onset of yellow, resulting in significant variations in driver behaviour. These variations, in turn, influenced the duration of braking and the time taken to cross the stop line from the onset of yellow.]]></description>
      <pubDate>Tue, 25 Mar 2025 09:28:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2516994</guid>
    </item>
    <item>
      <title>Reliability of C-ADAS and the importance of the acceleration function for cycling safety</title>
      <link>https://trid.trb.org/View/2508948</link>
      <description><![CDATA[Driving characteristics of bicyclists and motorists differ significantly in critical, uncritical and unaffected situations in road traffic. When bicyclists cross the path of right-turning motorists, bicyclists seem to mitigate conflicts that can develop into crashes, while motorists seem to avoid non-critical but close interactions that can develop into conflicts. This is one of the key findings of the evaluation of a recently developed and successfully tested cooperative driver assistance system (C-ADAS) that warns right-turning motorists of potential collisions. The warning is given by a special traffic light, which we called ‘amber light’, lighting up only in dangerous situations. Whether a situation becomes dangerous or not is determined by a decision tree, fed by the measured kinematics and specific surrogate measures of safety of the interacting road users. Most notably, the results demonstrate that criticality can be rated by measuring anticipation (or surprise) by computing the cross-power spectrum and applying entropy metric on the acceleration functions of the road users. However, one of the outcomes is that the time for the road users to perceive the amber light state might be too low to react properly. These findings can be used to improve the performance of such a C-ADAS.]]></description>
      <pubDate>Wed, 12 Feb 2025 09:00:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2508948</guid>
    </item>
    <item>
      <title>Empirical analysis of dilemma zone using high-resolution event data</title>
      <link>https://trid.trb.org/View/2459173</link>
      <description><![CDATA[The dilemma zone (DZ) has been physically characterized based on two divergent definitions: Type I and Type II. However, treating DZ differently based on these definitions may lead to inaccurate results of DZ boundary and subsequent safety analyses. Moreover, an integrated empirical assessment of Type I and Type II definitions for consistency in boundary quantification is not yet well-addressed. To this end, the authors empirically analyzed the two DZ definitions by comparing their boundary dynamics with approach velocity and time of day. First, the authors proposed a rule-based matching methodology with 92% accuracy to match actuation events between the advance and stop-bar detectors. This methodology was then applied to process two months of high-resolution event data from an intersection approach, yielding 28,700 vehicle arrivals on yellow. Results showed that 13.2% of approaching vehicles fall into an indecision zone or make Type I-contrary stop/run decisions at the yellow onset. The Type I and Type II DZ boundaries were temporally segregated and did not significantly overlap. The authors' novel findings indicate a lack of consistency in quantifying DZ and emphasize a need for data-driven quantification of the DZ boundary and its dynamics.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:39:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2459173</guid>
    </item>
    <item>
      <title>Data-Driven Approach for Prediction of Drivers’ Decision in Type-II Dilemma at Signalized Intersection</title>
      <link>https://trid.trb.org/View/2445120</link>
      <description><![CDATA[During the amber phase at signalized intersections, many drivers often face a dilemma when deciding whether to stop or go. This indecisiveness has significant safety issues; hence, predicting a driver’s decision, evaluating policies that supplement the driver’s decision-making process, and mitigating the dilemma zones at signal-controlled intersections are crucial. The present study develops multiple dilemma decision prediction models using statistical and data-driven machine learning (ML) approaches. Also, the models are developed while focusing on the internal validation and external transferability for in-field application to aid real-time performance. Several vehicular, geometric, and signal operational parameters derived from vehicular trajectory data from four study locations are used for the analysis. The modeling approach yields good performance results in predicting a driver’s decision during the amber phase. ML-based models are observed to yield better performance. Further, the statistical method provides statistically significant coefficients and corresponding elasticity values for several parameters used for evaluating and visualizing the effect of varying the parameter value on the location of the dilemma zone. These models are beneficial in assessing the effect of changes in operational policies. Whereas ML-based models yield the advantage of higher prediction accuracy, faster and robust predictions, and inherent quality of better understanding of the complexity in data, these models are more suited for real-time operation, driver assistance, and signal optimizations. The SHapley Additive exPlanations (SHAP) values that support measuring the effect of an individual parameter on prediction performance of the ML model are also studied.]]></description>
      <pubDate>Wed, 27 Nov 2024 13:43:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2445120</guid>
    </item>
    <item>
      <title>What would affect drivers’ stop-and-go decisions at yellow dilemma zones? A driving simulator study in Hong Kong</title>
      <link>https://trid.trb.org/View/2425648</link>
      <description><![CDATA[Yellow dilemma, at which a driver can neither stop nor go safely after the onset of yellow signals, is one of the major crash contributory factors at the signal junctions. Studies have visited the yellow dilemma problem using observation surveys. Factors including road environment, traffic conditions, and driver characteristics that affect the driver behaviors are revealed. However, it is rare that the joint effects of situational and attitudinal factors on the driver behaviors at the yellow dilemma zone are considered. In this study, drivers’ propensity to stop after the onset of yellow signals is examined using the driving simulator approach. For instances, the association between driver propensity, socio-demographics, safety perception, traffic signals, and traffic and weather conditions are measured using a binary logit model. Additionally, variations in the effect of influencing factors on driver behaviors are accommodated by adding the interaction terms for driver characteristics, traffic flow characteristics, traffic signals, and weather conditions. Results indicate that weather conditions, traffic volume, position of yellow dilemma in the sequence, driver age and safety perception significantly affect the drivers’ propensity to stop after the onset of yellow signals. Furthermore, there are remarkable interactions for the effects of driver gender and location of yellow dilemma.]]></description>
      <pubDate>Mon, 16 Sep 2024 08:55:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2425648</guid>
    </item>
    <item>
      <title>Modeling decision-making process of drivers during yellow signal phase at intersections based on drift–diffusion model</title>
      <link>https://trid.trb.org/View/2412962</link>
      <description><![CDATA[The decision-making behavior of drivers during the yellow signal phase has a significant impact on intersection safety. To analyze the decision-making process, the authors conducted surveys on driver behavior during yellow signal phase. A drift–diffusion model was established to analyze factors associated with driver decisions. The model can accurately predict driving decision outcomes (whether to proceed through the intersection during the yellow signal phase) and the decision-making times of different drivers. Driving data were collected using a driving simulator, including 15 participants in 210 tests in seven scenarios (3150 experimental samples). Drivers with similar driving behaviors were grouped. The model was validated using both in-sample and out-of-sample data for both individual and representative drivers. It was found that the error rate of the predicted data was approximately 7 %. Different arrival times had a significant impact on decision response time. Drivers tended to make faster decisions when the arrival time was less than 2 s due to the urgency of the decision. The findings can help understand the underlying cognitive mechanisms of driver behavior during the yellow signal phase.]]></description>
      <pubDate>Thu, 22 Aug 2024 15:09:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2412962</guid>
    </item>
    <item>
      <title>Understanding Drivers' Compliance Behavior: Data-Driven Assessment of Longer Yellow Intervals</title>
      <link>https://trid.trb.org/View/2414239</link>
      <description><![CDATA[Red-light running (RLR) behavior poses significant risks at signalized intersections and has emerged as a leading cause of intersection-related crashes. The Phoenix metropolitan area had 113 RLR-related fatalities and 9,320 injuries from 2014 to 2020. To effectively mitigate RLR violations and uphold the safety of all road users, it is crucial to investigate RLR behavior at local intersections, evaluate the impact of different signal timing parameters—such as the yellow interval—on the frequency of RLR violations, and, finally, identify effective countermeasures. This study investigated the effect of updating the yellow interval on the frequency of red-light violations. Twelve intersections within the City of Phoenix were carefully selected as study sites. Then, smart sensors were installed to collect various data types, such as signal timing parameters, the vehicle count, and RLR violation data. Based on the ITE 2020 guidelines, yellow intervals were adjusted at each intersection. The effects of increased yellow intervals on RLR violations were examined by utilizing a comprehensive experimental before-and-after design. The before-and-after study results indicated that increasing the yellow intervals significantly reduced the average frequency of RLR violations for both through and left-turn movements by 83% and 72%, respectively. The results of this research are instrumental in informing transportation agencies, enabling them to adopt evidence-based approaches to signal timing strategies that enhance intersection safety and effectively reduce RLR violations.]]></description>
      <pubDate>Mon, 12 Aug 2024 08:51:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2414239</guid>
    </item>
    <item>
      <title>Dilemma Zone Modeling Using Yellow-Onset Vehicular Trajectory Data</title>
      <link>https://trid.trb.org/View/2203852</link>
      <description><![CDATA[Dilemma zone is dynamically featured both in location and length at signalized intersections. This study presents a proof-of-concept development of the innovative methodology for characterizing the dynamics of dilemma zones using video-capture techniques. With availability of ground-truth video and extracted vehicle trajectory data, various dilemma zone contributing factors, such as vehicle type, speed at the onset of yellow indication, onset acceleration/deceleration, dilemma zone behavior (pass or stop), yellow duration, and arrival type can be identified and/or quantified. More importantly, the study quantitatively distinguishes and models the risky zone and option zone, both of which constitute a dilemma zone under varied speeds and yellow durations. The paper demonstrates a significant advancement in dilemma zone data analysis and modeling, which provides a solid basis for further research on modeling the dynamics of dilemma zones and loop detectors placement for dilemma zone protection problems in the future.]]></description>
      <pubDate>Thu, 25 Jul 2024 17:12:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203852</guid>
    </item>
    <item>
      <title>Extending the decision-making process during yellow phase from human drivers to autonomous vehicles: A microsimulation study with safety considerations</title>
      <link>https://trid.trb.org/View/2365357</link>
      <description><![CDATA[One of the main factors affecting the safety of signalised intersections is the stop/go behaviour during the yellow interval. Although previous research has exhaustively examined drivers' stop/go decision-making, the expected autonomous vehicles' (AVs') stop/go behaviour has not yet been thoroughly investigated. Through a series of simulation experiments developed for conventional and autonomous vehicles using different car-following, lane-changing, lateral placement and stop/go model parameter values, the authors examine here whether the default VISSIM stop/go parameter values can adequately replicate the observed drivers' behaviour at the considered intersection and assess the suitability of using the currently available options, albeit referring to human drivers, to simulate the expected stop/go behaviour of AVs. The authors also propose a policy framework for determining the desired behaviour of AVs in yellow interval, which is integrated into an AVs logic and achieved in the last simulation to explore the effect of automation on the stop/go outcome and, hence, on the safety level of signalised intersections. Several data analysis and modeling techniques were used for the formulation of certain scenarios, including binary choice models. The default stop/go parameter values were found unfit to replicate the observed stop/go behaviour and subjected to calibration. Compared to the currently available options, the proposed AVs logic proved to produce the most accurate results, in terms of the stop/go simulation outcome. Regarding the impact of automation on the stop/go outcome, the simulation experiments showed that AVs preferred a more conservative behaviour in favor of road safety, as indicated by the significant reduction (≈15%) in the number of vehicles crossing the stop line during the yellow light and zero instances of red light violation. However, compared to the conservative drivers represented by the default stop/go parameter values, AVs preferred a more rational behaviour in favor of intersection capacity without compromising road safety.]]></description>
      <pubDate>Fri, 10 May 2024 16:50:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2365357</guid>
    </item>
  </channel>
</rss>