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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>A dynamic bus lane strategy for integrated management of human-driven and autonomous vehicles</title>
      <link>https://trid.trb.org/View/2626083</link>
      <description><![CDATA[This study introduces a dynamic bus lane (DBL) strategy, referred to as the dynamic bus priority lane (DBPL) strategy, designed for mixed traffic environments featuring both manual and automated vehicles. Unlike previous DBL strategies, this approach accounts for partially connected and autonomous vehicles (CAVs) capable of autonomous trajectory planning. By leveraging this capability, the strategy grants certain CAVs Right-of-Way (ROW) in bus lanes while utilizing their “leading effects” in general lanes to guide vehicle platoons through intersections, thereby indirectly influencing the trajectories of other vehicles. The ROW allocation is optimized using a mixed-integer linear programming (MILP) model, aimed at minimizing total vehicle travel time. Since different CAVs entering the bus lane affect other vehicles’ travel times, the model incorporates lane change effects when estimating the states of CAVs, human-driven vehicles (HDVs), and connected autonomous buses (CABs) as they approach the stop bar. A dynamic control framework with a rolling horizon procedure is established to ensure precise execution of the ROW optimization under varying traffic conditions. Simulation experiments across two scenarios assess the performance of the proposed DBPL strategy at different CAV market penetration rates (MPRs). Results show that, compared to the benchmark strategy, the proposed DBPL strategy reduces private car travel time by up to 22% across two road scenarios, achieves further gains of up to 34% under higher traffic volumes, and remains virtually unaffected by dense bus arrivals, all while maintaining bus priority. It also adapts effectively to different bus stop locations and right-turn ratios, ensuring only the necessary number of CAVs enter the bus lane to optimize flow, and consistently surpassing existing methods in all tested conditions.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2626083</guid>
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    <item>
      <title>A cooperative decision-making method for CAVs from the perspective of opinion dynamics</title>
      <link>https://trid.trb.org/View/2622407</link>
      <description><![CDATA[Autonomous vehicles face significant challenges when dealing with severe conflicts or complex weaving patterns. Though cooperative decision-making can address conflicts, concerns remain regarding the practicality and the substantial computational burden. Additionally, the neglect of social rules hinders effective interaction between connected automated vehicles (CAVs) and human-driven vehicles (HVs). To achieve efficient cooperation in mixed-traffic flows, this paper selects the unsignalized roundabout as the test site and proposes a negotiation decision-making framework based on opinion dynamics, which consists of opinion formation, quantification, consensus, and implementation. Specifically, the rights-of-way are considered as opinions and evaluated using utility. All CAVs achieve consensus on the allocation of the rights-of-way through communication, and ultimately accomplish motion control via rolling optimization. The opinion consensus process employs a heuristic algorithm based on entropy-iteration, effectively promoting convergence towards the optimal solutions and significantly reducing computational burden. In order to enhance the social compliance, a Support Vector Machine (SVM) model is constructed using naturalistic driving datasets. And the intentions of human drivers are predicted and integrated into the whole framework. Comparative tests against rule-based and game-based methods show that the proposed approach demonstrates superior performance in terms of safety, comfort, efficiency, and economy. Especially in the systemic cost, it can reduce average delays by 12.4 % to 29.8 %. Finally, hardware-in-the-loop (HIL) experiments are carried out, highlighting the potential for real-world application.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622407</guid>
    </item>
    <item>
      <title>What They Don’t Know Can Kill Them: More Evidence on Why Pedestrian and Driver Knowledge of the Vehicle Code Should Not Be Assumed</title>
      <link>https://trid.trb.org/View/2635341</link>
      <description><![CDATA[Traffic safety researchers have long argued that driver behavior outweighs physical elements (such as road design) as a causal factor in motor vehicle collisions. A fundamental causal component of pedestrian-vehicle collisions is also behavior—that of the driver and that of the pedestrian. One determinant of this behavior may be whether the driver, the pedestrian, or both understand the motor vehicle code, which demarcates right-of-way in pedestrian-vehicle interactions. That is, inappropriate or unlawful behavior may occur because the law is not understood or is misunderstood. Previous studies have shown that drivers and pedestrians have a limited knowledge of pedestrian right-of-way laws. This research expands on these studies by specifically considering knowledge of right-of-way laws related to marked and unmarked crosswalks. Driver and pedestrian knowledge was assessed through intercept surveys and focus groups conducted in the San Francisco Bay Area. Results confirm that a substantial level of confusion exists with respect to pedestrian right-of-way laws. This confusion was exacerbated by intersections which had unstriped, or unmarked, crosswalks. Implications for engineering, education, and enforcement countermeasures in light of these findings are discussed and areas for further research are proposed.]]></description>
      <pubDate>Sun, 01 Feb 2026 16:31:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635341</guid>
    </item>
    <item>
      <title>Assessing the Impact of Transit Right-of-Way on Service Reliability via Segment-Level Data Integration and Ensemble Learning</title>
      <link>https://trid.trb.org/View/2658636</link>
      <description><![CDATA[Transit service reliability is a critical determinant of passenger satisfaction and system efficiency. While dedicated transit rights-of-way (ROW)—such as bus lanes and busways—are known to improve travel-time reliability, most existing studies are corridor-specific and do not generalize to system-wide planning. The analysis is often at the route level, ignoring potential reliability variation along routes. Moreover, the effects on travel-time variability of alternative ROW treatments, such as bus-on-shoulder, high-occupancy vehicle (HOV) lanes, and high-occupancy toll (HOT) lanes, remain underexplored. This study addresses these gaps by deriving segment-level reliability metrics using high-resolution automatic vehicle location data and automatic passenger count data for the entire transit network in the Minneapolis-St. Paul Twin Cities metropolitan area. A gradient boosting regression tree model is employed to evaluate how various ROW types, service characteristics, traffic conditions, and land use features affect travel-time variability. Results show that substantial reliability improvements occur only when over half of a route segment is dedicated to bus running, highlighting the limited impact of partial ROW implementations. The study also finds that bus-on-shoulder and HOV/HOT lanes offer limited reliability benefits under non-peak conditions. In addition to ROW, other factors, such as signal density and operating environments along route segments, also significantly affect their travel-time variability. The trained model can support scenario analysis and guide ROW planning by estimating the impacts of specific implementations and helping prioritize investments based on projected user benefits and reliability gains.]]></description>
      <pubDate>Tue, 27 Jan 2026 09:19:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658636</guid>
    </item>
    <item>
      <title>Legal Analysis of the Establishment of Exclusion Zones for Submarines in Indonesia’s Archipelagic Sea Lanes</title>
      <link>https://trid.trb.org/View/2601471</link>
      <description><![CDATA[AbstractThe Indonesian Archipelagic Sea Lanes (IASLs) are critical to international shipping and modern global trade, which depends on unobstructed access through vital maritime routes, including archipelagic sea lanes (ASLs). However, these lanes represent a strategic vulnerability for Indonesia as an archipelagic state during armed conflict, particularly when submarines, highly stealthy warships, are allowed submerged passage. This article examines the legal framework governing submerged transit by neutral submarines within belligerent ASLs during armed conflict. It identifies gaps in the existing law and proposes the establishment of an underwater exclusion zone for submarines as a potential solution, assessing its legality under international law. The article draws three primary conclusions. First, there is a legal lacuna regarding the permissibility of submerged submarine navigation in ASLs when the archipelagic state is at war. Second, the establishment of exclusion zones in ASLs by a belligerent coastal state is lawful under international law, although ambiguity remains about whether access for neutral shipping is mandatory. Third, an underwater exclusion zone is legally permissible, provided neutral submarines are granted a means of passage.]]></description>
      <pubDate>Tue, 23 Dec 2025 09:51:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601471</guid>
    </item>
    <item>
      <title>Toward Proactive-Aware Autonomous Driving: A Reinforcement Learning Approach Utilizing Expert Priors During Unprotected Turns</title>
      <link>https://trid.trb.org/View/2553253</link>
      <description><![CDATA[Given the complex nature of interaction under ambiguous right-of-way scenarios, the interactions between Autonomous Vehicles (AVs) and Human-driven Vehicles (HVs) present considerable challenges to the safety and efficiency of the traffic system. Existing AVs struggle to comprehend and apply common HV social norms, especially the proactive behavior exhibited by adept human drivers in ambiguous right-of-way scenarios. In this study, we propose a novel framework to leverage expert priors for proactive-aware decision-making in ambiguous right-of-way, merging Reinforcement Learning (RL) with parameterized modeling. Building upon unprotected-turning interactions from real-world driving datasets, we select typical cases under ambiguous right-of-way as human-expert priors, which are utilized to guide the learning of the RL agent. Then, a Hidden Markov Model (HMM), which is governed by interpretable parameters derived from expert priors, introduces human decision updating mechanism into AV strategy. Experimenting with typical driving tasks, our approach achieves balanced safety and efficiency in tackling ambiguities of right-of-way, with superior decision-making performance via the guidance of expert priors when compared with established baselines. Furthermore, the results indicate that the proposed method enables AVs to accelerate the convergence during the interaction by consistent probing and decision updates.]]></description>
      <pubDate>Thu, 04 Dec 2025 17:13:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553253</guid>
    </item>
    <item>
      <title>The characteristics of yielding decisions and visual attention in irritable and high right-of-way cognition drivers: The role of accident base rate</title>
      <link>https://trid.trb.org/View/2618246</link>
      <description><![CDATA[This study examined the interactions between accident base rate and right-of-way attitude on yielding decision in drivers and the visual attention characteristics of drivers with high levels of right-of-way cognition and elevated anger levels in response to right-of-way violations, using video clips of driving scenarios. Tobii pro fusion was employed to collect eye movement markers and reaction time data of 111 drivers. Conditions of low and high accident base rates were first established. The Drivers’ Attitudes of Right-of-way Questionnaire was used to measure drivers’ right-of-way attitudes. Right-of-way cognition, right-of-way emotion, time to first fixation, and total fixation duration on the violating vehicle by drivers in high accident base-rate were predictors of yielding decision. Drivers with high levels of right-of-way cognition and emotion had a lower rate of yielding and longer yielding reaction time. Under high accident base rate, longer time to first fixation was associated with a longer reaction time to yielding. Drivers with high levels of right-of-way cognition and emotion develop expectation-driven cognitive schemata due to their strong adherence to traffic rules. These maladaptive anticipatory frameworks impair both their detection speed for violating vehicles and the allocation of visual attention resources. Meanwhile, high accident base rate enhance drivers’ risk assessment (risk anticipation) capabilities toward traffic scenarios and increase willingness of right-of-way cognition to yield and foster adaptive visual recognition patterns and attentional resource allocation strategies. This study identified effective methods to improve yielding willingness and address maladaptive visual attention patterns of drivers exhibiting high levels of right-of-way cognition and emotion, which has important implications for optimizing traffic management practices and drivers’ training.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:19:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618246</guid>
    </item>
    <item>
      <title>Right-of-way interactions between automated vehicles and other road users in winter conditions – Insights from Oslo, Norway</title>
      <link>https://trid.trb.org/View/2624234</link>
      <description><![CDATA[This study was conducted during the initial phase of testing an on-demand, shared automated service in Oslo, Norway. It draws on video recordings and log-data (speed, driving status, GPS) from the automated vehicles (AVs) collected during three weeks in winter 2024/2025 to examine the nature and challenges of right-of-way interactions at a right-hand-priority T-intersection in an urban area. The main goal was to explore potential conflicts and risky situations between AVs and other road users in ways that can inform the development and eventual introduction of safe autonomous systems. Compared with the studies the authors conducted earlier in the Oslo region, the AVs in this pilot demonstrated notable improvements in their ability to operate in complex traffic environments. At the same time, the authors observed challenging situations involving negotiation, the assessment of other road users’ intentions as well as informal norms and mobility practices. Some of these situations required that the on-board operator disengaged the AV from automated mode to solve the situation. The authors selected seven illustrative examples of these and discussed them in more detail. It is worth noting that the study utilizes data collected during a short period of the testing phase, when disengagements may have reflected testing protocols or precautionary measures rather than technical limitations. As such, the findings should not be interpreted as indicative of how AVs would perform in routine operations without an on-board human operator. Nevertheless, they suggest continued progress and provide relevant insights into how AVs are adapting to real-world urban settings.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:19:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624234</guid>
    </item>
    <item>
      <title>Implementation of Multitemporal Synthetic Aperture Radar for Ground Hazard Risk Monitoring on Railway Right of Way</title>
      <link>https://trid.trb.org/View/2620575</link>
      <description><![CDATA[Railway transportation has been the backbone of national economies worldwide. When geohazards occur and damage the network, they affect railway operations, resulting in delays and detrimental social and economic effects. A potential tool for monitoring the vast network for geohazards is satellite-based radars. Interferometric synthetic aperture radar (InSAR) may be used to study a wide range of geophysical phenomena. Its ability to study geohazards is frequently constrained by several challenges stemming from adverse atmospheric effects and wave scattering associated with site conditions and terrain characteristics. The authors have developed the framework of a monitoring system that uses satellite radar imagery analysis for identifying geohazard-prone locations through continuous monitoring of large regions. This paper discusses one implementation of multitemporal InSAR techniques that includes the new concept of a “Rolling SAR Image Stack.” In addition, it introduces three postprocessing techniques that enable the detection of critical locations where geohazard failures may initiate along the railway right of way before an event takes place. A site characterization and classification guide is introduced to facilitate the selection of the most effective SAR analysis method for monitoring the area of interest. The guide considers on-site conditions affecting the quality and availability of radar data. This paper summarizes the investigations, methodologies, and approaches that led to the development of the workflow of the proposed monitoring system and demonstrates the ability of the proposed monitoring framework to identify critical locations of geohazard failure potential through implementation case studies.]]></description>
      <pubDate>Fri, 07 Nov 2025 11:30:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620575</guid>
    </item>
    <item>
      <title>Research on Collision Avoidance Decision-Making of Stand-On Ships Based on the Improved Velocity Obstacle Algorithm</title>
      <link>https://trid.trb.org/View/2592279</link>
      <description><![CDATA[Ship collision avoidance is a crucial aspect of maritime safety, especially when it comes to autonomous navigation systems. The subject of this study is the decision-making process for stand-on ships using the improved velocity obstacle algorithm, which incorporate real-time data to enhance collision avoidance effectiveness. Through the use of a unique ship collision risk model, the research integrates data from multiple heterogeneous sources to improve situational awareness and create a thorough understanding of the marine environment. Important collision risk factor elements such as ship speed ratio, relative bearing, relative distance, and the closest point of approach (CPA) are all included in this model. By quantifying the stages of ship contacts, the proposed approach successfully solves the ambiguity and randomness inherent in collision risk assessment. A range of encounter scenarios, such as crossing, head-on, and multiship scenarios, were simulated to show that the suggested strategy for stand-on ships is feasible. The results from these simulations validate the model’s efficacy, demonstrating its capability to handle diverse operational challenges and improve decision-making for autonomous ships. The findings highlight the model’s significant potential to enhance maritime safety by providing a more accurate and reliable collision avoidance strategy, which can lead to improved decision-making capabilities and safety protocols for autonomous navigation systems.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592279</guid>
    </item>
    <item>
      <title>An unstructured single-layer optimization approach for flexible right-of-way allocation and cooperative trajectory planning at signalized intersections</title>
      <link>https://trid.trb.org/View/2588358</link>
      <description><![CDATA[Existing methods for signal timing and vehicle trajectory coordination often rely on fixed-phase designs or leading vehicle guidance, limiting efficiency in dynamic traffic and multi-vehicle coordination. This study models signal timing as right-of-way allocation for each inbound lane at discrete time intervals and integrates trajectory planning into a mixed integer linear programming framework for joint optimization. By ensuring the isolation of conflicting flows and optimizing the coordinated release of non-conflicting traffic, the proposed method enhances intersection throughput while maintaining safety. To ensure the real-time applicability of the cooperative optimization strategy, this study employs an iterative Benders decomposition algorithm to reduce computational costs and mitigate the impact of prolonged solution times on optimization accuracy. Compared to leading vehicle guidance and fixed-phase cooperative optimization methods, the proposed framework reduces average delay by 13.37% and 23.4%. These results demonstrate that the proposed method effectively balances computational efficiency and traffic performance, making it a promising solution for real-world intersection management.]]></description>
      <pubDate>Tue, 23 Sep 2025 08:59:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2588358</guid>
    </item>
    <item>
      <title>Microscopic Right-of-Way Trading Mechanism for Cooperative Decision-Making: Theories and Preliminary Results</title>
      <link>https://trid.trb.org/View/2512241</link>
      <description><![CDATA[In this paper, a microscopic right-of-way trading mechanism (Micro-ROWTM) is developed to encourage cooperative behavior in mixed traffic, which consists of cooperative vehicles (CoVs) and non-cooperative vehicles (NCoVs). Micro-ROWTM encompasses the following functions: (i) detection of traffic conflicts and system-improving opportunities; (ii) participants reporting their attributes; (iii) right-of-way trade proposed by the system; and (iv) trade settlement and cooperative control. In the experiments, the proposed mechanism is tested using an illustrative ramp-merging example, in which each right-of-way trade proposal is offered to a group of participants comprised of a mainline leader, a mainline follower, and a ramp vehicle. Upon the acceptance of a proposal, the mainline leader and/or mainline follower agrees to behave cooperatively (yield) to the ramp vehicle, and receive monetary compensation from the ramp vehicle in return. To help design the trading mechanism, six important definitions are introduced and discussed, including (i) strong individual rationality, (ii) system efficiency and group efficiency, (iii) dominant-strategy incentive compatibility, (iv) social welfare maximization, (v) utilitarian envy minimization and (vi) payoff difference minimization. A linear programming-based trading and taxation model which satisfies the above definitions is then developed and solved. Using the Micro-ROWTM, when the flow rate is 1000veh/h for both the mainline and ramp traffic, the ramp vehicles can save 6.8% travel time and 27.4% fuel consumption on average, and the mainline vehicles can eventually obtain a positive payoff by compensation. From a system perspective, the ramp-merging section can save 107.5s of travel time and 2450.3g of fuel consumption per hour.]]></description>
      <pubDate>Thu, 10 Jul 2025 17:21:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512241</guid>
    </item>
    <item>
      <title>Incorporating behavioral adaptation of human drivers in predicting traffic efficiency of mixed traffic: a case study of priority t-intersections</title>
      <link>https://trid.trb.org/View/2563046</link>
      <description><![CDATA[]]></description>
      <pubDate>Tue, 10 Jun 2025 14:47:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563046</guid>
    </item>
    <item>
      <title>Value of Dedicated Right-of-Way: Transit Service Reliability and User Impacts</title>
      <link>https://trid.trb.org/View/2549184</link>
      <description><![CDATA[Transit services connect people to jobs and opportunities, fostering vibrant communities and multimodal travel along service corridors. A transit right-of-way (ROW) can help buses bypass congestion and stay on schedule. Many studies have proved that transit ROWs effectively improve service reliability and reduce user costs. However, these studies often focus on one or two service corridors, limiting comprehensive impact assessment. This project addresses this gap by investigating service reliability for all route segments across a transit system. The authors derived reliability metrics at the route segment level using high-resolution automatic vehicle location (AVL) and automatic passenger count (APC) data collected in the Twin Cities metropolitan area. The authors then collected and integrated data from various sources via spatial-temporal computing to capture service characteristics, operating environments, traffic conditions, and land-use features along route segments. The authors applied the Gradient Boosting Model (GBM) to examine nonlinear relationships between these factors and bus travel time reliability. Lastly, the authors used the trained model to estimate potential improvements in reliability with dedicated ROWs. Through these steps, the authors worked with members of the Technical Advisory Panel (TAP) to illustrate the methodology and demonstrate its utility for transit agencies. Specifically, the results proved that the ratio of bus lanes and busways was associated with more reliable travel time along route segments. The authors also found that route segments along a few service corridors with unreliable services can greatly benefit from implementing a dedicated ROW.]]></description>
      <pubDate>Thu, 08 May 2025 09:41:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2549184</guid>
    </item>
    <item>
      <title>Intersection passing strategies for human-driven and autonomous vehicles in mixed traffic using DEA</title>
      <link>https://trid.trb.org/View/2548936</link>
      <description><![CDATA[In this paper, the authors propose a right-of-way optimization model considering multi-objective Data Envelopment Analysis (DEA) evaluation for intersections in mixed driving environments with automated and human driving. The authors consider average speed, number of cars, penetration of automated vehicles, queuing pattern, left-turn rate, and number of buses as factors influencing intersection right-of-way. They comprehensively consider the per capita delay, travel time and traffic volume as the optimization objectives, and then determine the weights of the three optimization objectives for each strand of traffic flow, and calculate the cross-benefit by interchanging the weight evaluation through the Crossing Efficiency Evaluation Method (CREE) to determine the optimal order of traffic flow in each direction at the intersection. In this paper, the optimization strategy is compared with existing benchmarks (e.g., actuated control) using SUMO simulation software, and the simulation results show that the proposed optimization strategy is able to shorten the per capita delay and travel time at intersections in order to improve the efficiency of the traffic flow compared to actuated control and the First-Come, First-Served strategy.]]></description>
      <pubDate>Mon, 05 May 2025 12:52:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548936</guid>
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