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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Can lane-changing preferences of connected and autonomous vehicles induce spontaneous platoon formation?</title>
      <link>https://trid.trb.org/View/2704123</link>
      <description><![CDATA[Connected and Automated Vehicles (CAVs) achieve a headway gain when following other CAVs. This study defines two key factors: the CAV cluster factor and the headway gain. We analytically examined the capacity of a single-lane Manual Vehicle (MV)-CAV mixed flow related to these factors and the CAV market penetration rate. Furthermore, this paper reveals that this headway gain induces a preferential lane-changing behavior in CAVs, making them more inclined to choose to follow other CAVs. This preference promotes the spontaneous formation of CAV platoons. By analyzing four types of lane-changing scenarios, the study derives an expression for the CAV cluster factor in multi-lane MV-CAV mixed flows. Simulation experiments validated the theoretical findings and demonstrated that the CAV cluster factor in multi-lane mixed flows is primarily determined by the headway gain and the CAV market penetration rate. It exhibits independence from the specific longitudinal control laws of CAVs, the car-following behavior of MVs, and whether lane-changing decisions incorporate the speed of the leading vehicle. Although CAVs’ lane-changing preferences can contribute to the formation of larger spontaneous platoons, this effect remains limited. Under higher traffic densities, lane-changing opportunities are significantly diminished, which inhibit the influence of lane-changing preferences, causing the degree of CAV clustering in multilane flows to approach that observed in single-lane scenarios.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704123</guid>
    </item>
    <item>
      <title>How passenger travel mode choice dynamics shape nighttime public transport service planning? A game-theoretic perspective</title>
      <link>https://trid.trb.org/View/2694720</link>
      <description><![CDATA[Understanding passenger behavioral dynamics is essential for designing efficient and demand-responsive nighttime public transport (NPT) services. This study develops a multi-population evolutionary game framework to analyze heterogeneous passengers’ NPT mode choice and its underlying mechanisms. Replicator dynamics are employed to derive analytical threshold conditions governing behavioral evolution. The insights are incorporated into an operational optimization model that endogenizes passenger responses in service planning. A Beijing case study shows that, under dual-equilibrium conditions, outcomes are influenced by initial preferences, but strong cross-population interactions can weaken path dependence, highlighting the potential for early-stage interventions. Moreover, marginal deviation costs regulate coordination: higher costs eliminate discoordinated choices and enable dual coordinated equilibria, while lower costs sustain heterogeneous mode choices. Behavioral heterogeneity from the value of time and travel distance supports differentiated services, such as long-distance express metro and short-distance feeder buses. Fare and headway exhibit behavioral tolerance thresholds: once passenger acceptance is exceeded, mode choices can shift abruptly, leading to inefficient system performance. Importantly, incorporating endogenous behavioral dynamics into planning reduces total travel costs by 52.96% relative to a behavior-agnostic model. This study underscores the necessity of incorporating behavioral dynamics into NPT planning to support efficient, adaptive, and sustainable services.]]></description>
      <pubDate>Thu, 30 Jul 2026 10:08:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694720</guid>
    </item>
    <item>
      <title>Statistical Analysing for the Impact of Headway on Level of Service on 2-Lane Highways</title>
      <link>https://trid.trb.org/View/2671530</link>
      <description><![CDATA[A microscopic traffic flow parameter called headway can be used to represent driver behaviour, safety evaluations, capacity estimates, and level of service assessments. Although time headway analysis in mixed and heterogeneous traffic conditions has been the subject of numerous research, but the impact of time headway on the level of service has not received nearly as much attention. In order to manage road network and traffic effectively, it is crucial to describe the variation or impact of time headway on capacity and the level of service. The present study deals with a comprehensive analysis of headway distribution patterns including best-fit distribution found using statistical software. The Kolmogorov–Smirnov test has been carried out for this investigation. Results show that at LOS-C and LOS-D, log-normal is the best-fitted distribution. The best-fit headway distribution for LOS-B is achieved in general extreme value. The study concludes from the cumulative distribution of vehicles with respect to headway, 40% of vehicles in LOS-B have a headway of three seconds or less, 55% of vehicles in LOS-C have a headway of three seconds or less, and 70% of vehicles in LOS-D have a headway of three seconds or less.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671530</guid>
    </item>
    <item>
      <title>Value of time multipliers for walk time, wait time, service headway, displacement time, reliability and congested travel time: review and meta-analysis of worldwide evidence</title>
      <link>https://trid.trb.org/View/2680681</link>
      <description><![CDATA[This study provides the most comprehensive review and meta-analysis of time multipliers that are fundamental to transport planning and appraisal. The attributes covered are: walk time, wait time, access/egress time, connection time, out-of-vehicle time and service headway, which are largely characteristics of public transport trips; travel time in various degrees of congestion and parking search time, which are features of car travel; displacement time; and a range of variables related to travel time variability. The evidence assembled covers 2872 multipliers drawn from 606 studies across 52 countries over the years 1963 to 2023.It was found that many of the multipliers vary between Revealed and Stated Preference data, with the former almost always larger, and over time, indicating a reduction where an effect was discerned. Other influences were detected from mode used, journey purpose and distance. The estimated meta-model implies credible multipliers across a range of illustrative scenarios. It can be used to deliver multipliers for use in planning and appraisal where none exist or to benchmark existing or emerging evidence. When compared with official recommendations, the meta-model’s implied multipliers challenge the long-established multipliers of two for walk and wait time, supporting a reduction to 1.75 or 1.50 depending upon the emphasis placed on Revealed or Stated Preference data. The results support official headway and late time multiplier recommendations but suggest refinements for the reliability ratio and congested time multipliers along with new guidance for displacement time multipliers.]]></description>
      <pubDate>Thu, 25 Jun 2026 09:40:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680681</guid>
    </item>
    <item>
      <title>Coordinating transfers between bus and metro train in real time: A logic-based branch-and-bound method</title>
      <link>https://trid.trb.org/View/2674361</link>
      <description><![CDATA[The concept of Mobility-as-a-Service and on-demand public transit increasingly depends on integrating multiple transportation modes to offer seamless door-to-door travels. However, coordinating these modes in real time to address fluctuating demand and disturbances remains a major technical challenge. We tackle this issue by formulating a mixed-integer quadratic programming model to generate coordinated bus and train adjustment strategies. The model captures key factors such as traffic dynamics, passenger loads, and vehicle overtaking. To address the computational challenge of the mixed-integer property, we incorporate logic-based concepts into a branch-and-bound framework, analyzing the logical relationships among variables to improve solution efficiency. This logic-based branch-and-bound method exploits the distinct strengths of discrete and continuous components. It employs logical inference to guide the search, incorporates domain reduction to accelerate computation, and constructs reduced continuous optimization problems to efficiently update bounds and estimate logical values. Computational results demonstrate that the proposed coordinated adjustment strategy effectively improves vehicle punctuality and headway regularity, while reducing the number of stranded non-transfer and transfer passengers. The solution method demonstrates desirable computational efficiency in real-world settings, which is suitable for real-time applications.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674361</guid>
    </item>
    <item>
      <title>Active-Disturbance-Rejection Cooperative Control for Multi-Train System With Constraints and Collision Avoidance</title>
      <link>https://trid.trb.org/View/2659029</link>
      <description><![CDATA[In practical multi-train systems, external disturbances and internal uncertainties are typically present, posing significant challenges to safety and efficient operation of the trains. This paper investigates the active-disturbance-rejection cooperative control problem for multi-train systems with simultaneous collision avoidance, velocity constraints, and input constraints. A distributed control algorithm is proposed to enable each train to track the desired velocity while maintaining desired safe inter-train distances. By integrating an active disturbance compensation mechanism, the total effects of external disturbances and internal uncertainties can be estimated in real time, thereby significantly enhancing system robustness. The stability analysis is mainly performed based on stochastic matrix theory and Lyapunov stability theory. It is shown that the tracking errors for desired velocity and relative position of all trains will eventually converge to a bounded region under the influence of uncertainties and external disturbances, while collisions are avoided and the velocity and input of each train are maintained within their respective constraint sets. Finally, numerical simulations are provided to validate the effectiveness of the proposed approach.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659029</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>Knowledge of state-recommended following-distance rules</title>
      <link>https://trid.trb.org/View/2689157</link>
      <description><![CDATA[Rear-end accidents are often attributed to the following vehicle not allowing sufficient headway to react in the event of a sudden change in velocity by the lead vehicle. Accordingly, most U.S. state DMVs offer seconds-based guidelines for safe following distances such as a ‘three-second rule’. However, studies have demonstrated that headways shorter than those recommendations are common. While numerous explanations can account for these observations, fundamentally, drivers must be aware of following distance recommendations in order to follow them. Here, we tested drivers’ knowledge of the following-distance rule (FDR). Results demonstrated that only 55.2% of survey respondents knew their state had an FDR, only 13.4% knew that it was based off of a seconds method, and only 2.4% accurately identified their state’s specific FDR. Factors associated with FDR knowledge included number of years of licensure and self-reported knowledge of driving rules.]]></description>
      <pubDate>Sun, 03 May 2026 18:19:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689157</guid>
    </item>
    <item>
      <title>Headway regularity as an attribute for classifying bus drivers</title>
      <link>https://trid.trb.org/View/2663000</link>
      <description><![CDATA[Different indices have been proposed in the literature to characterize headway regularity. These metrics aggregate the headway variability for a service, but none can be directly associated with a specific driver. This paper seeks to understand drivers' influence on a service's regularity. To do so, we propose four regularity indices related to a driver's performance and use the Hierarchical Clustering Analysis method to generate a classification of drivers according to their contribution to the headway regularity during the operation of a service. We characterize each class based on the driver's attributes such as age, years of experience as a driver, and years in the bus company, and those attributes associated with the operation, such as number of services per day and period of the day. The results show consistency in the classification obtained, with nearly 90% of drivers remaining in the same regularity classes regardless of the index.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663000</guid>
    </item>
    <item>
      <title>Real-time bus control of urban transit networks with overtaking and passenger transfers: A decomposition solution method combined with spatial branch-and-bound</title>
      <link>https://trid.trb.org/View/2659531</link>
      <description><![CDATA[Frequent disturbances and demand fluctuations can result in unreliable bus operations and transfer services. This paper investigates real-time bus control for urban transit networks under dwell and travel time disturbances. The objective is to optimize bus timetable adjustments to minimize the total bus departure deviation, headway deviation, and passenger waiting in a time horizon. We propose a mixed-integer nonlinear programming model under rolling horizon, which incorporates both traffic and passenger load dynamics while accounting for bus overtaking, passenger transfer, and bus capacity limitations. To quickly obtain high-quality solutions to satisfy real-time requirements, we develop an efficient decomposition method combined with spatial branch-and-bound. This method reduces computational complexity by breaking down the network-level problem into smaller-scale line-level problems. It iteratively updates the solution and objective function values by solving a network-level feasibility recovery problem, ensuring coordination among line-level solutions through iterations. Extensive computational experiments demonstrate that our solution algorithm exhibits superior computational efficiency compared to a state-of-the-art solver across different network scales, facilitating a real-time implementation. Furthermore, our approach outperforms conventional schedule- and headway-based control strategies, effectively reducing departure deviation, headway deviation, and passenger waiting time.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659531</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>On Modelling and Analysing Rail Moving-block Signalling by Means of Hybrid Petri Nets</title>
      <link>https://trid.trb.org/View/2682065</link>
      <description><![CDATA[The railway industry is facing the growing demands to improve safety, capacity, and efficiency while adopting advanced digital and automation technologies. This paper presents a Hybrid Petri Nets (HPNs) model to simulate train-following dynamics under a moving-block signalling system. The main purpose is to maintain the safe headway between trains in real-time even during disruptions like unexpected speed reductions. The model allows follower trains to adjust speed adaptively based on the leader’s position, ensuring safety without relying on emergency braking. A numerical simulation is performed to evaluate performance under various minimum headway conditions. The results show how train speed and headway vary under different scenarios, and improve line capacity under moving-block operation. Future work will focus on integrating further real-world features, including station stops, heterogeneous trains, and uncertainties modelling to better reflect operational conditions.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682065</guid>
    </item>
    <item>
      <title>Operational control approach for connected-autonomous-bus line in mixed public transit environment</title>
      <link>https://trid.trb.org/View/2643307</link>
      <description><![CDATA[The use of connected and autonomous buses (CABs) is growing, and surface public transit is being transformed into a mixture of manually driven buses (MDBs) and CABs. We aim to establish a hierarchical optimal control model to ensure the efficient operation of CABs in a mixed public transit environment. The first level outputs the planned speed to improve the headway uniformity. Considering the operational uncertainties of MDBs and the output parameters from the first level, the second level minimises the speed changes and the number of CABs entering the node queue and determines the speed control and signal priority schemes. We propose a solution algorithm based on rolling optimisation and establish simulation cases based on the bus lines in Beijing. Compared to both the uncontrolled scheme and the single-speed control scheme, this approach significantly reduces headway deviation and the number of CABs entering the node queue.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643307</guid>
    </item>
    <item>
      <title>Coordinated dual-objective transit signal priority: a deep reinforcement learning approach</title>
      <link>https://trid.trb.org/View/2643284</link>
      <description><![CDATA[Transit Signal Priority (TSP) has been widely used for reducing transit delays for decades. Since reliability is valued equally as travel time, a dual-objective coordinated (DC) TSP is developed to adaptively optimize transit headway adherence and travel time simultaneously over consecutive intersections. This is the first attempt at using a centralized agent deep reinforcement learning (RL) framework in solving a coordinated TSP optimization problem. Decentralized control algorithms using multi-agent RL are also developed as baseline scenarios. TSP algorithms are trained and tested in a stochastic microsimulation environment within Aimsun Next for a corridor segment in Toronto with a transit line experiencing high service variability. DC TSP demonstrates a clear promise in reducing headway variability and travel time at different traffic levels. It highlights the importance of coordinating TSP actions at consecutive intersections. It is also shown to be robust, providing effective control under various configurations of bus stop locations.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643284</guid>
    </item>
    <item>
      <title>Crash Risk Assessment in a Mixed Traffic Environment of Autonomous and Human-Driven Vehicles</title>
      <link>https://trid.trb.org/View/2674203</link>
      <description><![CDATA[This study investigates the interaction between autonomous vehicles (AVs) and human-driven vehicles (HDVs) during the transition phase of autonomous vehicle deployment. Utilizing two open-source datasets, Lyft L5 and Waymo, it analyzes car-following behaviors, revealing significant differences in space headway, time headway, and response times between AVs and HDVs. AVs typically maintain larger following gaps, which can impact traffic flow and safety dynamics in a mixed traffic environment. Furthermore, this research calibrates the Wiedemann 99 psychophysical car-following model to better replicate real-world driving characteristics and assess crash risks in such environments using dynamic Bayesian network. The results highlight that the implementation of AVs yields significant safety benefits – a remarkable 33.8% and 66.3% reduction in crash risk at 75% AV penetration, underscoring the positive impact of AVs in traffic safety improvement.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674203</guid>
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