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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Visual exploration and head-movement strategies in children and adults during road-crossing decisions</title>
      <link>https://trid.trb.org/View/2735150</link>
      <description><![CDATA[Children are vulnerable road users and face an increased risk of injury as pedestrians compared to adults. Children are considered to have less extensive visual exploration and make more unsafe crossing decisions than young adults. Apart from walking speed, exploratory head movements may explain why children face an increased risk when making street-crossing decisions. 44 adults aged 18–36 years (M = 23.34, SD = 3.46) and 30 children aged 7–11 years (M = 9.57, SD = 1.25) completed the 10-metre walk test and two key street-crossing tasks: gap acceptance and hazard perception, which were completed using an established virtual reality (VR) paradigm. Head movements were recorded using the VR device. The authors showed that adults turned their heads more often and were faster to respond in gap acceptance and hazard perception tasks compared to children. Analyzed separately, children’s head turn frequency and excursion significantly predicted hazard perception and gap acceptance over and above walking speed. For adults, only head turn frequency significantly predicted gap acceptance and hazard perception over and above walking speed. Results suggest that adults may complete a final check of traffic before making a street crossing decision, whereas children may not complete this check. Street-crossing is a complex task and as children age, head movements become more deliberate when exploring for a potential hazard and gap in traffic. As such, training interventions should incorporate head movements to help improve children’s street crossing skills.]]></description>
      <pubDate>Thu, 20 Aug 2026 09:54:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735150</guid>
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    <item>
      <title>Analysis of the influence of traffic variables on lane change manoeuvres decision-making in real-world traffic scenarios</title>
      <link>https://trid.trb.org/View/2736816</link>
      <description><![CDATA[This paper presents an analysis of lane changing maneuvers under real traffic conditions, focusing on how drivers adapt their behavior across different driving regimes. Using naturalistic driving data collected on a high-capacity road in Madrid (Spain), the research explores the role of traffic context in decision-making focusing on gap acceptance, vehicle dynamics, safety metrics and motivational factors. To analyze these dynamics, drivers were exposed to six controlled traffic configurations designed to reproduce representative real-world highway scenarios with different surrounding vehicle distributions, inter-vehicle distances, and relative speeds. Driver behavior was analyzed across three distinct maneuver phases, corresponding to car-following, anticipation, and lane change execution. The results show that traffic density and the surrounding environment significantly shape driver strategies. Higher traffic complexity leads to longer anticipation phases, greater dispersion in timing, and reduced safety margins. While Time-to-Collision (TTC) offers general insights into risk, gap-based metrics proved more stable and informative for predicting the actual execution of the maneuver. This study contributes to the understanding of context-dependent lane-change behavior using controlled naturalistic driving scenarios. The findings support the development of predictive models and the design of advanced driver assistance systems (ADAS) and automated vehicles, enabling safer and more adaptive lane-change algorithms that better account for driver intent and traffic dynamics.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:02:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736816</guid>
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    <item>
      <title>Critical Gap Estimation and Its Effects on Capacity and Safety at a High-Speed Uncontrolled T-Intersection</title>
      <link>https://trid.trb.org/View/2671904</link>
      <description><![CDATA[Unsignalized intersections are critical locations in terms of the risk and vulnerability involved in incorrect assessment of gaps by drivers on minor roads and higher speeds of vehicles on major roads. The speed profile of minor road and major road vehicles with subject movements under consideration is analyzed. Right-turning movement from a minor road is a critical maneuver with several conflict points. This paper examines the performance level and risk associated with right-turn movements from minor roads by estimating the critical gap and capacity with due consideration to the vehicle types. Analyzing drivers’ gap acceptance behavior is essential for designing safer and more efficient intersections in terms of performance. The model is formulated for gap acceptance of right-turn movements from minor roads by logistic regression. The critical gap, the minimum time interval deemed safe by drivers, is crucial for estimating intersection capacity and associated risk toward planning prospective traffic management measures. By analyzing critical gaps for different vehicle types, estimating the probability of gap acceptance, and developing predictive models, this research aims to enhance traffic flow and safety at unsignalized intersections. The findings will inform urban traffic planners, contributing to enhancing the design and overall traffic efficiency of an unsignalized intersection.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671904</guid>
    </item>
    <item>
      <title>Assessing Entry–Circulating Conflict Severity at Roundabouts in Mixed Traffic Using Post-Encroachment Time and Speed Profiles</title>
      <link>https://trid.trb.org/View/2714288</link>
      <description><![CDATA[Traffic roundabouts are generally associated with improved safety because of reduced conflict points and lower operating speeds; however, their performance under mixed traffic conditions in low- and middle-income countries remains inadequately understood. Safety assessment in such environments is constrained by heterogeneous traffic and limited availability of reliable crash data. Although surrogate measures of safety based on traffic conflicts provide a viable alternative, conflict severity is often defined using arbitrary thresholds. This study presents a data-driven framework to classify and predict conflict severity at urban roundabouts operating under mixed traffic conditions. Entry–circulating vehicle interactions were extracted from videographic data, and conflict severity was classified using k-means clustering based on post-encroachment time, entry speed, and circulating speed. The derived severity labels were used to develop a random forest model incorporating behavioral, traffic, and geometric variables. The model achieved an accuracy of 87.8% and an area under the receiver operating characteristic curve of 0.91. Entry speed and circulating speed emerged as the most influential predictors, followed by gap acceptance. The framework avoids predefined severity thresholds and supports proactive safety assessment within a Safe System perspective.]]></description>
      <pubDate>Thu, 18 Jun 2026 08:54:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714288</guid>
    </item>
    <item>
      <title>Wait or cross? Understanding the influence of behavioral tendencies, trust, and risk perception on pedestrian gap-acceptance of automated truck platoons</title>
      <link>https://trid.trb.org/View/2705464</link>
      <description><![CDATA[Although automated trucks have the potential to improve freight efficiency, reduce operating costs, and address driver shortages, the convoying of two or more trucks has raised considerable concerns around pedestrian safety. This study conducted a controlled experiment to examine the influence of behavioral tendencies, trust, and risk perception on pedestrian intention to cross in front of an automated truck platoon. A total of 603 subjects participated in a virtual reality video-based questionnaire survey. By fusing the merits of structural equation modeling and artificial neural networks, a two-stage, hybrid model was developed to examine complex relationships between latent variables and gap-acceptance behaviors. The results indicated that the subjects watched an average of five vehicle gaps before starting crossing and the average time gap accepted was about 5.35 s. Risk perception not only played the dominant role in shaping pedestrian crossing decisions, but also fully mediated the effects of behavioral tendencies and trust on gap-acceptance. Participants who frequently violated traffic rules were more likely to accept a smaller time gap, while those who showed positive behaviors toward other road users tended to wait for a larger time gap. Participants who often committed errors, showed aggressive behaviors, and held greater trust in the safety of automated trucks generally reported a lower level of perceived risk of road-crossing in front of automated truck platoons. Building on these findings, a range of tailored countermeasures were proposed to ensure safer and smoother interactions between pedestrians and automated truck platoons.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705464</guid>
    </item>
    <item>
      <title>Traffic risk spatial-temporal evolution mechanism and dynamic prediction approach from the vehicle-to-vehicle interaction perspective</title>
      <link>https://trid.trb.org/View/2701436</link>
      <description><![CDATA[In intelligent connected vehicles, lane-changing decisions and trajectory planning often prioritize local safety, neglecting the broader impact of vehicle interactions on traffic flow. However, interaction patterns between vehicles significantly affect both lane-change risk and traffic dynamics. Moreover, existing models often overlook spatial dependencies in highly interactive environments. This study addresses these gaps by proposing a classification of four interaction patterns based on vehicle acceleration and inter-vehicle gap trends. Using the TOD trajectory dataset, the authors analyze the dynamic evolution of traffic risk across these patterns and introduce a spatiotemporally-aware risk prediction model. A Spatial Durbin Model captures the spatial influence of neighboring vehicles on ego-vehicle risk, while a Gaussian Mixture Model–Hidden Markov Model (GMM-HMM) dynamically adjusts transition probabilities to forecast risk levels and vehicle motion states. This integrated approach enables short-term, macroscopic traffic flow risk forecasting. Results show that aligned interaction intentions lead to slower risk dissipation and higher risk levels. Compared to the GMM-HMM and Recurrent Neural Network (RNN) models that do not account for spatial effects, the proposed model achieves a prediction accuracy of 85.5% in a 5-second prediction window, which is 13.5% and 5.0% higher, respectively. Although it is slightly lower than the 86.5% achieved by the Temporal Fusion Transformer (TFT), the computational time is reduced by 1.814 s, demonstrating better real-time performance. The model shows good prediction accuracy across four interaction modes: steady-state interaction, strong competitive interaction, strong cooperative interaction, and weak cooperative interaction. The accuracy rates for predicting the risk of subsequent vehicles within 5 s are 87.8%, 76.4%, 84.7%, and 81.9%, respectively. This study provides valuable insights into the mechanisms through which different vehicle interaction patterns influence traffic flow, and offers essential guidance and optimization directions for lane-change trajectory planning in autonomous driving systems.]]></description>
      <pubDate>Wed, 27 May 2026 10:48:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701436</guid>
    </item>
    <item>
      <title>SafePass: Efficient emergency vehicle passage with minimal disruption to traffic flow</title>
      <link>https://trid.trb.org/View/2697070</link>
      <description><![CDATA[Emergency vehicle passage in congested urban networks poses a dual challenge: ensuring rapid response while minimizing disruption to surrounding traffic. This study addresses this challenge in the context of Connected Autonomous Emergency Vehicles (CA-EVs), proposing SafePass, a lightweight distributed framework for seamless CA-EV passage through decentralized, cooperative maneuvering of surrounding Connected Autonomous Non-Emergency Vehicles (CA-NEVs). At its core, SafePass employs the Target Lane Potential (TLP), a novel utility-based metric combining lane-choice utility with probabilistic gap acceptance, augmented by a cascade-aware penalty that suppresses upstream shockwaves triggered by gap-creation maneuvers. Evaluated in Simulation of Urban Mobility (SUMO) using synthetic traffic and real-world trajectory data from the Next Generation Simulation (NGSIM) US-101, Wuhan University Next Generation Simulation (WUT-NGSIM), and modified Waymo Open datasets, SafePass consistently clears lanes well before the CA-EV’s Estimated Time of Arrival (ETA), reducing CA-EV travel time by up to 30% compared to baselines while lowering surrounding vehicle travel time by 8%–10%, demonstrating that safety and efficiency need not be traded off.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697070</guid>
    </item>
    <item>
      <title>A Statistical Approach to Estimate Gap Acceptance Parameter at Three-Legged Uncontrolled Intersection</title>
      <link>https://trid.trb.org/View/2581520</link>
      <description><![CDATA[The estimation of the gap acceptance parameter for right-turning movements at uncontrolled T-intersections is the main focus of this paper. It takes a lot of effort to investigate an intersection that is unsignallised or is uncontrolled. In general, a driver's perception of the priority at an intersection is based on the volume of traffic, the design of the intersection, and the speed of the vehicle on main and minor approaches. Three uncontrolled T-intersections in the field were recorded as video in order to acquire the data. Accepted gaps, rejected gaps, vehicle types, and traffic composition are the data that were extracted from the video. The accepted and rejected gaps are fitted with various statistical distributions. The best-fitted model is recommended for the accepted and rejected gap based on goodness of fit tools, and the critical gap is estimated. Additionally, the critical gap value determined by best-fitted distributions (log-normal distribution) is compared to the values determined using Indo-HCM (Indo-HCM: Indian Highway Capacity Manual (Indo-HCM). CSIR-Central Road Res. Institute, New Delhi. (2017)). The value of the gap obtained using detailed traffic parameters can be further used for risk prediction to reduce the severity of crashes at uncontrolled intersections under mixed traffic conditions.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:47:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581520</guid>
    </item>
    <item>
      <title>Spatial-temporal Analysis of Lane-Changing Behavior and Time Gaps on Highways using Aerial Observations</title>
      <link>https://trid.trb.org/View/2682169</link>
      <description><![CDATA[Drone observations on highways offer exceptional insights into spatio-temporal microscopic traffic events. In dense and congested traffic conditions, the analysis of lane-changing behaviors across different highways yields significant insights into driver behavior. This study analyzes lane-changing maneuvers and associated time gaps using high-resolution drone data from highways in Germany, China and the United States. The approach defines the criticality of the vehicle maneuvers in the different traffic phases as defined in Kerner’s three phase traffic theory. By assessing the safety of over 10,000 lane changes, the findings highlight the elevated risks associated with overtaking and passing maneuvers. A model is proposed to equally redistribute time gaps during lane-changing maneuvers, to systematically reduce the risks both the lane-changing and all surrounding vehicles. The results indicate around 40 % of cars maintain a time gap smaller than 1.8 s, while only 32 % of lane changes are executed as safe maneuvers. A comparison of the time gap ratio between the leading and following vehicle during lane changes reveals that, in synchronized flow, drivers tend to accept smaller time gaps ahead while maintaining larger time gaps behind, shifting the risk predominantly to the lane changing vehicle. By incorporating the proposed hypothetical model for a lane-change assistance system, the proportion of lane changes classified as risk-free could be nearly doubled to 62%.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682169</guid>
    </item>
    <item>
      <title>Exploring pedestrian gap acceptance behaviour using immersive CAVE experiments: A multilevel logit regression model</title>
      <link>https://trid.trb.org/View/2690045</link>
      <description><![CDATA[Unsafe crossing behavior is a key contributory factor to pedestrian injuries. Understanding the influences of potential factors on pedestrian crossing behavior is essential. Previous studies have examined the relationship between pedestrian behavior, road environments, and traffic characteristics. However, the influences of psychological factors, such as safety perception, on pedestrian decision-making are rarely considered. This study investigates the influences of environmental factors, vehicle attributes, personal demographics, and safety perceptions on the gap acceptance behavior of pedestrians at mid-block crossings using a hybrid experiment and attitudinal survey approach. For instance, a Cave Automatic Virtual Environment (CAVE) method is employed to enhance the immersive experiences of 3-dimensional road environments and dynamic traffic characteristics for pedestrians. A multilevel logit regression method is then employed, controlling for the interdependency between multilevel factors: (1) Participant level: demographics, safety attitude; (2) Trial level: road environment, traffic control, vehicle mix; and (3) Observation level: vehicle class, gap size, and waiting time, in the association measure. Results indicate that the likelihood of gap acceptance increases with pedestrian age, risk-taking attitude, speed limit, gap size, and waiting time. In contrast, the likelihood of gap acceptance decreases with the increased perceived control, presence of on-street parking, and presence of heavy vehicles. These findings shed light on effective remedial measures, such as targeted road safety education and local area traffic management, that can mitigate pedestrian crash risk at high-risk locations with frequent pedestrian-vehicle interactions. Therefore, overall pedestrian safety can be improved, and walkability can be enhanced in the long run.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:57:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2690045</guid>
    </item>
    <item>
      <title>Crossing Confidently: The Role of Walking Speed and Visual Exploration in Older Adults’ Street-Crossing Decisions</title>
      <link>https://trid.trb.org/View/2677686</link>
      <description><![CDATA[Older adults (> 65 years old) are vulnerable road users who are overrepresented in collisions with vehicles. Apart from walking speed, visual function and visual exploration factors may explain the street-crossing behaviors contributing to the increased risk. Although previous research compared street-crossing behavior profiles between younger and older adults, no studies investigated the relative role of visual function and visual exploration of older adults in detecting hazards and identifying safe gaps. 101 older adult pedestrians (Mage = 68.83, SDage = 5.25; 60–83 years old) completed a suite of visual function and physical tasks. Hazard perception and gap acceptance tasks were administered via an established pedestrian street crossing virtual reality (VR) paradigm. Head and eye movements were measured using VR. The authors showed that walking speed did not predict hazard perception and gap acceptance suggesting walking speed was not factored when crossing the street, which may increase their risk. Contrast sensitivity significantly predicted hazard perception response time; whereas, visual acuity, head turn excursions, and the number of fixations predicted the number of correct responses within the hazard perception time window. For gap acceptance, the results showed that head turn frequency significantly predicted response time and the number of correct responses in the gap acceptance window. The findings show that contrast sensitivity is associated with improved hazard perception, whereas head movements is associated with safe crossing decisions in older adults. Street crossing is complex with specific visual exploration and visual function factors contributing uniquely to gap acceptance and hazard perception in older adult pedestrians.]]></description>
      <pubDate>Tue, 24 Mar 2026 16:18:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2677686</guid>
    </item>
    <item>
      <title>Exploring the Relationship Between Pedestrian Perceived Speed and Gap Acceptance at Midblock Crossings in Bhubaneswar City</title>
      <link>https://trid.trb.org/View/2669871</link>
      <description><![CDATA[Pedestrian safety at midblock crossings relies on accurate speed and gap judgment, as misjudgement can lead to unsafe decisions and increased conflict risk. This study examines the relationship between perceived speed and gap acceptance at six-lane divided roads in Bhubaneswar, India. Two field experiments conducted with 324 participants assessed speed estimation (estimated the approaching vehicle’s speed), and gap acceptance (decided whether to accept or reject the gap for safe crossing), yielding 1,858 speed estimations and 1,917 gap decisions. Results show pedestrians underestimate vehicle speeds by 1.66 kmph, leading to accepting shorter gaps (2.65 seconds). Older pedestrians, females, and frequent crossers accepted longer gaps and showed better speed perception. The generalized linear model revealed that crossing frequency and interactions among vehicle type, age, gender, and speed difference significantly influenced the gap acceptance rate. Based on results, marked crossings, speed calming measures, and awareness programmes can effectively reduce risky pedestrian crossing behaviour.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669871</guid>
    </item>
    <item>
      <title>Estimating the Critical Gap for U-Turns at Uncontrolled Median Openings</title>
      <link>https://trid.trb.org/View/2669836</link>
      <description><![CDATA[Critical gap is an essential traffic parameter for assessing the capacity of U-turning vehicles at uncontrolled median openings and plays a vital role in U-turn facility design. Inaccurate driver gap acceptance data in U-turn designs can lead to traffic congestion and increase the risk of severe accidents. This study aimed to estimate the critical gap for U-turns at uncontrolled median openings by analyzing real-world traffic behavior across three distinct U-turn patterns in Bangkok using various estimation methods. The results showed that the Raff method produced the lowest critical gap estimates during both peak and off-peak hours (3.24 to 5.39 seconds), followed by the Maximum Likelihood Estimation method (4.29 to 6.69 seconds), the Greenshield method (4.75 to 6.75 seconds), and the Acceptance Curve method, which yielded the highest estimates (6.75 to 9.30 seconds). The findings also revealed that as direct traffic volume increased, the estimated critical gap values from all methods tended to decrease, and differences in U-turn patterns influenced the critical gap values. Among the methods evaluated, the Maximum Likelihood Estimation method demonstrated the highest consistency in critical gap estimation under varying traffic conditions.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669836</guid>
    </item>
    <item>
      <title>Pedestrian Impatience and Its Impacts on Gap Acceptance Decisions at Unrestricted Crossings</title>
      <link>https://trid.trb.org/View/2659340</link>
      <description><![CDATA[The majority of vehicle-pedestrian crashes occur at unrestricted crossings which are largely functioned by the pedestrian’s gap acceptance behavior. As the waiting time increases, a pedestrian often becomes impatient and eventually accepts a shorter vehicular gap than usual increasing the crash risk. In this background, the present study proposes a novel methodology for the estimation of this pedestrian impatience. It also includes a case study where the proposed methodology was implemented based on gap acceptance/rejection decisions made by a total of 206 pedestrians with diverse attributes. The results show that with every 1-second increase in the waiting time, male and female pedestrians intend to accept a 0.07 sec and 0.03 sec shorter vehicular gap respectively. Similarly, the overall impatience of young, middle-aged, and elderly pedestrians was estimated as 0.10, 0.06, and 0.02 sec/sec respectively. However, carrying luggage was found to be an insignificant factor for pedestrian’s impatience.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659340</guid>
    </item>
    <item>
      <title>Heavy Truck Gap Acceptance and Critical Gap Length Evaluation at Congested Roundabouts</title>
      <link>https://trid.trb.org/View/2643068</link>
      <description><![CDATA[Heavy trucks are believed to experience increased delays when entering congested roundabouts due to their increased size and more restrictive operating characteristics as compared to passenger cars. The present study evaluated 164 h of video data to transcribe 2,626 heavy truck movements at single-lane roundabouts across Oregon and Washington. Gap acceptance and gap rejection timings were evaluated to quantify heavy truck accessibility when entering congested roundabouts. Further, a variety of heavy truck classifications were observed and evaluated individually, with the most common being the WB-40, WB-50, WB-62, WB-67, WB-67D, and WB-92D. Within each of these classifications, gap acceptance behavior differed. Average rejected gap lengths were 3.84 s, while average accepted gap lengths were 8.69 s, indicating heavy trucks often accepted gaps that were over two times larger than the prior gaps. Critical gap length assessment was carried out following Raff’s method on an individual-classification basis to compare critical gaps across heavy truck classifications. Critical gap lengths for heavy trucks ranged from 5.4 to 6.4 s, approximately two to three times larger than that of passenger car critical gap values of 2.2 and 2.6 s documented in prior research. Additionally, heavy truck critical gap requirements were found to increase proportionately with the heavy truck size. The WB-40 was the smallest heavy truck assessed and was found to have a critical gap of 5.4 s. Conversely, the WB-92D was the largest heavy truck and was associated with a critical gap of 6.4 s. These findings indicate that roundabout locations featuring high heavy truck volumes may need additional considerations due to the operational restrictions of these larger vehicles, and that the assessment of roundabout implementation should include analysis of the vehicle classifications using these facilities to most optimally design for the current and future demand.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643068</guid>
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