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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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      <title>Characteristics of Mixed Traffic Flow in Highway Tunnels as Affected by Changes in Illumination</title>
      <link>https://trid.trb.org/View/2681378</link>
      <description><![CDATA[In a mixed traffic flow environment, the tunnel entrance and exit sections are prone to changes in traffic flow characteristics due to differences in illumination and the complexity of vehicle-following behavior. To investigate the characteristics of mixed traffic flow at tunnel entrances and exits affected by changes in illumination, a car-following model for human-driven vehicles (HDVs) was constructed based on actual vehicle tests and fuzzy controllers. Considering the degradation phenomenon of intelligent connected vehicles (ICVs), an ICVs car-following model was constructed. Finally, a mixed traffic flow model based on cellular automata and actor-critic (AC) algorithms was constructed. Through simulation, the characteristics of the impact of illumination on mixed traffic flow at the entrance and exit sections under different ICVs penetration rates were analyzed and evaluated in terms of traffic efficiency and stability. The results show that the illumination value changes drastically at the tunnel entrance, and the illumination value inside the tunnel fluctuates between 5 and 10 lx; under the influence of illumination change, the speed of vehicles at the tunnel entrance decreases and the following distance increases, and the speed of vehicles in the middle of the tunnel and the following distance are relatively smooth; when the penetration rate of ICVs in the mixed traffic flow inside the tunnel reaches 100%, the maximum flow rate of the traffic flow is improved by 1.4 times, and the standard deviation of the speed of the vehicles is positively correlated to the penetration rate of the ICVs. The standard deviation of vehicle speed is positively correlated with the penetration rate of ICVs, which is reduced by 85.45%. ICVs reduce the influence of illuminance on the traffic flow, and effectively reduce the speed fluctuation of the vehicle at the entrance and exit; it can be seen that the participation of ICVs significantly improves the average speed and the maximum flow of the traffic flow, reduces the speed fluctuation of the traffic flow, and also significantly improves the safety of the traffic flow under the condition of high penetration rate.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681378</guid>
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    <item>
      <title>Assessing Pavement Crash Susceptibility for Consideration in Pavement Management Decisions</title>
      <link>https://trid.trb.org/View/2680579</link>
      <description><![CDATA[Traffic crashes remain a major threat to road safety, with many crash-related casualties and large economic losses occurring every year. Generally, crashes are complex events involving interactions between a wide range of factors, including those related to the driver, vehicle, roadway, traffic, and weather at the time and place of the crash. Whereas numerous road safety studies investigated the contribution of various factors to crash frequency and severity, a better understanding of the combined effect of pavement condition on crash occurrence is needed to assist pavement managers identify appropriate pavement safety improvement projects. Pavement surface condition factors are of particular interest to highway agencies because they fall directly within their control and can be addressed through maintenance and rehabilitation (M&R) projects. This study introduces crash susceptibility as a new parameter useful for incorporating safety in pavement management decisions. In this study, crash susceptibility is defined as the probability of a crash occurring on any given pavement segment within a given year. Logistic regression was employed to estimate the likelihood of crash occurrence on asphalt concrete pavement (ACP) with and without surface treatment (ST) or seal coat as a function of the pavement condition (roughness, macrotexture, skid resistance, and distresses), while considering traffic volume, road geometrics, and environmental conditions. This study utilized three years (fiscal years 2021–2023) of crash, pavement condition, and site condition data from Texas. The results show that for both asphalt pavement types, increased roughness contributes to higher crash susceptibility, whereas increased skid resistance contributes to lower crash susceptibility. However, the effects of these pavement conditions are more significant on pavements without ST or SC. This study also shows that higher macrotexture is associated with a reduction in crash susceptibility for pavements without ST or SC. Of the individual pavement distresses examined, only potholes show a significant increase in crash susceptibility, and this effect is observed only for pavements with ST or SC. Other pavement distresses, including rut depth, raveling, flushing, cracking, failures, and distress score, were found to have no significant impact on crash susceptibility. Highway agencies can use this study to assess the influence of pavement surface conditions on crash susceptibility and to support data-driven decisions for roadway maintenance and safety improvements.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680579</guid>
    </item>
    <item>
      <title>Investigating the Factors Affecting Traffic Violations: A Study of Demographic and Environmental Influences</title>
      <link>https://trid.trb.org/View/2679054</link>
      <description><![CDATA[This study investigates the factors influencing eight types of traffic violations in Isfahan, Iran—speeding, not wearing a seatbelt, cell phone use while driving, red-light running, wrong-way driving (e.g., driving in the opposite direction on a one-way street), not carrying required documents (e.g., car insurance, driving license, and vehicle registration), illegal reversing (e.g., reversing on highways), and ignoring police stop signals—using comprehensive traffic ticket data (580,289 records) covering the entire year of 2015. A multinomial logit (MNL) model is applied, considering demographic and environmental factors. Results show that older drivers were less likely to speed but more likely to commit violations such as red-light running. Males were more likely to speed, and higher education levels were correlated with increased likelihood of violations such as red-light running. Local drivers were more prone to seatbelt and cell phone violations than were interprovincial drivers. Furthermore, the road type and time of day environmental factors significantly influenced violation patterns. Intercity roads observed more speeding and document-related violations, whereas roads within cities had higher occurrences of seatbelt and cell phone use. Morning hours were associated with higher speeding, and evening and night hours saw increased red-light running. Public holidays were linked to more relaxed attitudes, leading to higher rates of violations such as speeding and illegal reversing. This analysis sheds light on an area for which extremely limited data are available.]]></description>
      <pubDate>Tue, 09 Jun 2026 14:43:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679054</guid>
    </item>
    <item>
      <title>SALane: Speed and Accuracy Balanced Detection of Degraded Lane Markings Based on Visual Images</title>
      <link>https://trid.trb.org/View/2675138</link>
      <description><![CDATA[As a fundamental technology in the field of autonomous driving, lane detection plays a critical role in various advanced driver-assistance systems, including lane keeping, lane departure warning, lane change assistance, and forward collision warning. However, due to vehicle load, lane lines are prone to damage, making lane line detection more difficult. This study proposes SALane, an improved row-based ultra-fast and lightweight lane line detection network aiming at balancing ultra-fast speed and accuracy. Specifically, the Inception-v3 network is employed as a feature extractor, where the input image is partitioned into a grid with significantly fewer cells than pixels via anchor points. This design enables efficient localization of lane lines directly on the grid. Furthermore, Otsu’s method is utilized to automatically determine an optimal image binarization threshold. Additionally, a hybrid approach combining random sample consensus and least-squares fitting is introduced to enhance the robustness and accuracy of lane line modeling. Cross entropy is also used to attenuate the effect of imbalance between lane lines and background categories, and a simple calculation method of lane marking degradation rate is proposed. On the CULane benchmark, SALane achieves a total accuracy of 79.6% at a speed of 60.2 frames per second, which outperforms the compared algorithms by 10% in accuracy and demonstrates a well-balanced performance between precision and speed. Further, this study explores the effect of the degree of lane marking degradation on the detection results, and the algorithm performance is stable when the damage category is less than 3. However, as the number of damage categories increases to 4 and 5, the performance of the algorithm decreases sharply.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675138</guid>
    </item>
    <item>
      <title>Framework for Autonomous Driving Trajectory Prediction via Physics-Constrained Data Fusion and Spatiotemporal Encoding</title>
      <link>https://trid.trb.org/View/2675000</link>
      <description><![CDATA[With the rapid development of artificial intelligence, autonomous driving has advanced significantly. Vehicle trajectory prediction, a key component of autonomous driving, directly affects the quality of planning and decision-making in dynamic traffic scenarios and is crucial for intelligent transportation systems. This paper proposes a collaborative prediction framework integrating physical and data-driven models. The physical model, enhanced by driving style parameters, better characterizes vehicle kinematics and driving styles for improved trajectory prediction. The data-driven model employs a spatiotemporal feature decoupling module with a dual-stream attention mechanism to extract and fuse temporal and spatial features from historical trajectories, enabling accurate future trajectory prediction. Through a bidirectional constraint framework, the two models achieve collaborative parameter optimization: the physical model provides kinematic feasibility constraints to ensure predictions align with real-world motion laws, while the data-driven model compensates for unmodeled dynamic factors, addressing the limitations of physical models in complex scenarios. Experimental results on the NGSIM and HighD data sets validate the superiority of the proposed method, providing a novel solution for trajectory prediction in autonomous driving.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675000</guid>
    </item>
    <item>
      <title>Driver Merging and Lane Utilization Behavior under Zipper Merge Lane Control</title>
      <link>https://trid.trb.org/View/2674260</link>
      <description><![CDATA[Work zone lane closures require drivers to determine when and where to merge from a lane that is about to close to an adjacent open lane. The “zipper merge” strategy encourages drivers to stay in their lanes until reaching a defined merge area, where drivers alternate merging, analogous to a zipper. The zipper merge has been shown to provide better operational performance than the standard “early merge” scenario, reducing congestion and speed differentials between lanes. However, there is considerable variability in driver familiarity and behavior when encountering a zipper merge. To that end, this study evaluated the impacts of using supplemental portable changeable message signs (PCMS) to encourage drivers to follow the zipper merge strategy. Driver behavior was assessed through a series of field studies that compared vehicle lane utilization and merging behavior. The utilization rate of the soon-to-close lane was evaluated under three scenarios, which included (1) standard static zipper merge signage; (2) static signage with a PCMS displaying “USE BOTH LANES DURING BACKUP” upstream of the merge area; and (3) standard signage with two PCMS, with the second (downstream) sign displaying “MERGE HERE/TAKE TURNS” close to the start of the taper. A regression model was estimated to further investigate lane utilization while accounting for the effects of traffic volume, density, and the presence of heavy vehicles in the open and soon-to-close lanes. Installing an upstream PCMS improved lane utilization compared to static signs alone; however, the additional PCMS closer to the taper showed negligible impacts on lane utilization.]]></description>
      <pubDate>Tue, 02 Jun 2026 13:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674260</guid>
    </item>
    <item>
      <title>Resilience Enhancement of an Urban Rail Transit Network by Jointly Optimizing Restoration Sequences and Bus Bridging Services</title>
      <link>https://trid.trb.org/View/2674258</link>
      <description><![CDATA[Enhancing the urban rail transit network (URT) resilience under disruptions is crucial for improving its ability to respond to such events. However, prior studies have rarely enhanced the URT network resilience by jointly optimizing the restoration sequences of failed components and bus bridging services (BBSs). To address this gap, a URT network resilience enhancement strategy combining a restoration sequence and extended BBS optimization (ESCRB strategy) is developed herein to improve the URT network resilience under disruptions. The real-world Chengdu subway network is utilized as an example to validate the effectiveness of the proposed ESCRB strategy. Results imply that the travel weight between stations is critical in assessing the network resilience. The proposed ESCRB strategy can effectively enhance the URT network resilience by simultaneously optimizing restoration sequences and extended BBSs. After employing the ESCRB strategy, the network resilience metrics under random and deliberate disruptions are decreased by 83.96% and 81.80%, respectively. Finally, a sensitivity analysis is conducted to discuss the impact of parameters on the effectiveness of the formulated ESCRB strategy, and some practical implications are provided to improve the URT network resilience.]]></description>
      <pubDate>Tue, 02 Jun 2026 13:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674258</guid>
    </item>
    <item>
      <title>Employing the Mean-Field Approximation Technique in Multiagent Reinforcement Learning to Control Signalized Intersections in an Urban Network</title>
      <link>https://trid.trb.org/View/2673024</link>
      <description><![CDATA[Traffic signal control is a difficult task to ensure the performance of traffic networks in major cities around the world. Multiagent reinforcement learning (MARL) is a promising approach for traffic light management. As the number of agents increases, the learning process becomes impossible because of the curse of dimensionality and the interactions between agents. To solve this, we introduce a novel decentralized MARL-based approach combining the double deep Q network with the mean-field approximation technique (MFA). Our model eliminates the overestimation problem of the traditional DQN by using dual estimators during training. In addition, it also reduces model complexity by using the MFA to approximate the interaction within the population of agents as the interaction between a single agent and the average effect from neighboring agents. Our proposed method is compared against other algorithms to test its effectiveness. This study also provides an analysis of the influence of using different traffic generation tools (OD2Trips, DUArouter, Marouter, and DUAIterate) on the model performance. Experimental results demonstrate their effectiveness and robustness over other algorithms in terms of waiting time, average speed, and queue length.]]></description>
      <pubDate>Fri, 29 May 2026 14:09:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673024</guid>
    </item>
    <item>
      <title>Accounting for Mode Choice Heterogeneity under Travel Demand Management Strategies in a Developing Country</title>
      <link>https://trid.trb.org/View/2673029</link>
      <description><![CDATA[This study examines differences in work trip mode choice behavior under the combined implementation of congestion pricing (a push measure) and metro system introduction (a pull measure) in Mumbai, India. A combined revealed and stated preference (RP–SP) data set is used to estimate a random parameters latent class choice model (RP-LCCM), enabling the identification of distinct behavioral segments within the population. The results reveal two latent classes. Class 1 (43.48%) comprises younger and lower-income individuals who are highly cost-sensitive and less time-sensitive. They are willing to tolerate longer travel and access times and show a higher inclination toward using public transport, particularly metro services. Class 2 (56.62%) represents older, higher-income, and more educated respondents who are more time-sensitive and less cost-sensitive. This class exhibits a stronger preference for private modes such as cars and two-wheelers, valuing convenience and reduced travel time. Satisfaction with travel air quality is also higher in Class 2 (82%) compared to Class 1 (67.4%), reflecting perceptual and lifestyle differences. The estimated scale factor between RP and SP data indicates consistency in behavioral responses across data sets. The findings demonstrate significant heterogeneity in mode choice, showing that the impact of transport demand management (TDM) measures such as congestion pricing and metro expansion varies across income and life-cycle groups. The study emphasizes the importance of incorporating socioeconomic diversity and behavioral segmentation in policy formulation to promote efficient and equitable urban mobility.]]></description>
      <pubDate>Fri, 29 May 2026 14:09:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673029</guid>
    </item>
    <item>
      <title>Characterization of the Mechanical Behavior of Runway Strips during Veer-Off Incidents</title>
      <link>https://trid.trb.org/View/2673027</link>
      <description><![CDATA[Runway excursions pose risks that can implicate the operational safety of landings and takeoffs, cause damage to aircrafts, and result in financial and human losses. To mitigate these risks, it is essential to evaluate the mechanical behavior of runway strips, considering the stresses generated by the interaction between landing gears and the surface. This study aims to characterize the interaction between aircraft landing gears and the surfaces of runway strips during landing and takeoff veer-off. Simulations were conducted using the finite element method with the SIMULIA ABAQUS Student Edition 2020. The developed model considered eight structural configurations of runway strips composed of different materials and the impact of four different aircraft models on the surface, resulting in a total of 32 analyzed combinations. The simulations evaluated the influence of resilient modulus (MR), layer thicknesses, and different landing gear configurations on the surface deformations of the runway strips. Results indicated that the analyzed structures presented adequate structural capacity without significant displacements. A sensitivity analysis was conducted considering structures with surface layer thicknesses of 0.15, 0.25, and 0.35 m, with 12 MR combinations and four aircraft, totaling 144 simulations. It was observed that displacements vary nonlinearly as a function of MR, with thickness effects being more pronounced at lower MR values. The findings revealed that surface displacements on the runway strips are influenced by MR, layer thickness, and specific aircraft characteristics. Aircraft with lower operational weights may cause greater displacements due to landing gear configuration and load distribution. Additionally, the interaction between MR and thickness showed that less rigid materials exhibit greater displacements in thicker layers, while more rigid materials are less impacted.]]></description>
      <pubDate>Fri, 29 May 2026 14:09:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673027</guid>
    </item>
    <item>
      <title>Metal Organic Frameworks as Antiaging Agents for Bitumen: Investigating the Inhibition of Oxidation Reaction</title>
      <link>https://trid.trb.org/View/2673017</link>
      <description><![CDATA[Bitumen aging leads to increased brittleness and reduced flexibility, which significantly compromises the durability and long-term performance of pavements. This study investigates the potential of metal–organic frameworks (MOFs) as antiaging additives for bitumen, with an emphasis on their porous structures and selective adsorption behavior. Three representative MOFs, namely Metal–Organic Framework-5 (MOF-5), Zeolitic Imidazolate Framework-67 (ZIF-67), and Universitetet i Oslo-66 (UiO-66), were synthesized and characterized by scanning electron microscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, and thermogravimetric analysis. Their performance was compared with that of carbon nanotubes, which served as a conventional nanomaterial benchmark. The influence of these materials on the physical and chemical properties of bitumen was evaluated through viscosity measurements, Fourier-transform infrared analysis, and standardized aging tests. The results demonstrate that MOF-modified bitumen exhibits improved high-temperature flowability and substantial suppression of carbonyl and sulfoxide formation during oxidative aging. Among the tested materials, ZIF-67 provided the most pronounced antiaging effect, whereas carbon nanotubes primarily increased viscosity but were less effective in mitigating oxidative degradation. Three potential inhibition pathways, including inert gas oxidation blocking, reductive gas-induced reverse aging, and targeted adsorption combined with catalytic degradation, are proposed to explain the observed antiaging behavior. These findings indicate that metal–organic frameworks, particularly ZIF-67, are promising and sustainable modifiers for extending the service life of asphalt pavements. Future work will include long-term field validation and further elucidation of the molecular mechanisms underlying MOF-induced antiaging effects.]]></description>
      <pubDate>Fri, 29 May 2026 14:09:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673017</guid>
    </item>
    <item>
      <title>Structural Failure of Pretensioned Concrete Sleepers under Cyclic Loading Due to Water Flow in Cracks</title>
      <link>https://trid.trb.org/View/2669689</link>
      <description><![CDATA[Pretensioned concrete sleepers are vulnerable to structural failure when flexural cracks develop in moisture-rich environments. This study presents experimental evidence showing that water ingress in cracked regions leads to accelerated degradation under cyclic loading. Tests conducted on full-scale sleepers and small-scale prisms revealed that (1) debonding between tendons and concrete significantly increases compressive stresses, and (2) moisture reduces the concrete’s compressive strength. Notably, post-tensioned sleepers exhibited greater resistance to failure under similar conditions. Tendon corrosion, while a long-term concern, was not a contributing factor within the observed failure timeframe. These findings support recommendations for improved drainage and favoring unbonded post-tensioned systems in high-moisture settings.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669689</guid>
    </item>
    <item>
      <title>Modeling Lane-Wise Headway Distributions to Formulate Policies for Improving Traffic Operations at Uncontrolled Median Openings</title>
      <link>https://trid.trb.org/View/2666717</link>
      <description><![CDATA[This research investigates the lane-specific time-headway distributions of flow-through traffic near uncontrolled median openings (UMOs), with a focus on the effects of varying U-turn volumes. UMOs pose significant operational and safety challenges due to conflicts between U-turning and flow-through traffic. Traffic data were collected from several urban UMOs with diverse geometric and operational characteristics. A comprehensive statistical analysis was conducted to identify the most suitable headway distribution models for both affected and unaffected zones. The results reveal notable variability in headway distributions influenced by traffic volume, speed, lane configuration, and traffic composition. The study also establishes correlations among key traffic parameters such as vehicle type, volume, speed, lane usage patterns, and time headway across four- and six-lane urban roadways. The findings offer important implications for traffic operations at UMOs. It is recommended that heavy vehicles be assigned to lanes away from the median to minimize interference with U-turning traffic. In zones with reduced time headways due to high U-turn volumes, threshold values should be defined, and geometric improvements such as acceleration lanes or elevated U-turn lanes should be implemented. Where geometric enhancements are not feasible, regulatory measures such as yield signs are suggested. For UMOs with U-turn volumes exceeding 300 vehicles per hour, signalization is advised to reduce collision risks. Intelligent traffic systems should enforce right-of-way rules and provide clear guidance through signage. Additionally, AI-based monitoring should be employed to detect and address unsafe lane-changing behavior in real time. During peak periods, manual traffic control is recommended to manage U-turn operations effectively.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666717</guid>
    </item>
    <item>
      <title>An Online Train Control Method Based on the Shrinking Horizon Model Predictive Control Framework</title>
      <link>https://trid.trb.org/View/2666714</link>
      <description><![CDATA[This paper introduces an online optimal controller for train operation that considers dynamic train information. The optimal control problem is solved using a shrinking horizon model predictive framework, enabling real-time integration of operation information, such as temporary speed restrictions and real-time train interactions. The original nonconvex optimization problem is transformed into a much easier solution in each prediction range, while the transformed convex optimization problem does not lose the optimality of its solution. A complete train speed trajectory can be generated online by continuously resolving the optimal control problem at each sampling step. If the optimization problem becomes infeasible due to prediction errors, infeasible journey time, and unreasonable braking requirements, the objective function or constraint can be adjusted to ensure the proper functioning of the controller. The proposed method’s effectiveness and robustness are validated through numerical simulations using real-world railway data.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666714</guid>
    </item>
    <item>
      <title>Integrated Control Strategy of CAV-Dedicated Lane Balancing Efficiency and Safety in Expressway Merging Areas</title>
      <link>https://trid.trb.org/View/2666782</link>
      <description><![CDATA[To alleviate traffic congestion caused by on-ramp vehicles merging into the mainline in expressway merging areas under connected and automated vehicle (CAV) dedicated lane settings, an integrated entry speed adjustment–merging game control (ESA-MGC) control strategy is proposed. This strategy combines on-ramp ESA and CAV-dedicated lane MGC. The ESA strategy dynamically adjusts vehicle speed to proactively create merging gaps, improving on-ramp CAV efficiency, while the MGC strategy employs a game-theoretic approach to coordinate multiple CAVs merging into the dedicated lanes, resolving decision conflicts and optimizing space for on-ramp vehicles. Results show that ESA improves the lane change success rate by 19.23% compared to the gap acceptance model and by 6.82% compared to model predictive control. Under varying CAV penetration rates, MGC improves overall speed by at least 24.26% and reduces the overall time integrated time-to-collision by at least 7.57%. Sensitivity analysis confirms robust performance even at 85% compliance rate, demonstrating significant improvements in traffic efficiency and safety.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666782</guid>
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