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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7U3RvcHBlZCB0aW1lIGRlbGF5cyZxdW90OyIgb3JpZ2luYWxfdmFsdWU9IiZxdW90O1N0b3BwZWQgdGltZSBkZWxheXMmcXVvdDsiIC8+PC9maWx0ZXJzPjxyYW5nZXMgLz48c29ydHM+PHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8+PC9zb3J0cz48cGVyc2lzdHM+PHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg==" rel="self" type="application/rss+xml" />
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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Automating Average Stopped Delay Estimation Using Image Analysis of Actual Traffic Flow at Signalized Intersections</title>
      <link>https://trid.trb.org/View/2165696</link>
      <description><![CDATA[Average stopped delay for a given signalized intersection approach is the average time a vehicle waits at the traffic light and it constitutes a part of the measure of effectiveness of signalized intersections. The exploratory work done by two authors of this paper using images created by the animation feature of the CORSIM traffic simulation software indicated that such application can actually produce stopped delay estimates commensurate with the results of the ITE method. However, CORSIM simulation images do not have parallax problems and vehicle color and size and pavement color were practically the same for the entire evaluation field. The three methods (Gap, Gap-hybrid, Motion method) developed in this study are able to overcome to an acceptable level these problems associated with actual traffic flow images. They were tested with the two image data sets that were taken at two different locations with different camera angles. The performance of the three methods varies depending on the quality of image, camera angle, and calibrated parameter values used for each method. Nevertheless, in both cases they were able to produce average stopped delays similar to those estimated by the ITE manual method. At present the software that executes the three methods analyzes one approach lane at a time using digitized still images taken from analog video films; however, it can be expanded to analyze multiple approach lanes and digital images dynamically fed by digital traffic monitoring cameras. With this automated procedure the traffic engineer can estimate average stopped delays in his or her office, greatly saving time, money and manpower necessary for conducting field data collections.]]></description>
      <pubDate>Sun, 29 Mar 2026 17:20:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2165696</guid>
    </item>
    <item>
      <title>Dynamic sharing of bus lanes before intersections based on a presignal and bus positioning system</title>
      <link>https://trid.trb.org/View/2643238</link>
      <description><![CDATA[In this paper, a dynamic sharing control method for bus lanes before intersections based on a presignal and real-time bus positioning system is proposed. The presignal is set at a certain distance before the intersection, and there is an opening for general cars to enter the bus lane under the control of the presignal. The bus positioning system detects the position of the nearest bus before the intersection in real time. The bus delay and car delay under the dynamic sharing of bus lanes are analyzed. An algorithm for the dynamic sharing control of bus lanes before intersections is proposed to reduce the total delay, including bus delay and car delay. Four schemes are compared through an example, and the results show that the proposed method achieves the shortest total delay and has the least impact on bus travel while improving the capacity of general cars at the intersection.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643238</guid>
    </item>
    <item>
      <title>Integrated optimization of dynamic lane allocation and signal timing at urban intersections</title>
      <link>https://trid.trb.org/View/2643236</link>
      <description><![CDATA[Allocating spatiotemporal resources dynamically is deemed to be an effective technique for accommodating traffic arrivals at urban intersections. This paper develops a two-layer optimization framework for integrated lane allocation and signal timing at isolated intersections. The lane allocation at the upper-layer is formulated as an integer linear programming to minimize the sum of the critical flow factor. Whereas the lower-layer signal control problem is formulated as an integer quadratic programming to minimize the average vehicle delay. An iterative solution procedure is then introduced. Considering that frequent lane switching may affect the control performance, this paper proposes a dynamic implementation scheme to regulate the lane switching frequency. Numerical tests show that the proposed integrated optimization method outperforms the baseline method in reducing delays by 19.08%. Moreover, for the integrated optimization, further applying the proposed dynamic implementation scheme reduces the lane switching frequency, which effectively balances switching frequency and overall control performance.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643236</guid>
    </item>
    <item>
      <title>Regulating jaywalking behaviour in adaptive traffic signal control using a novel deep reinforcement learning approach</title>
      <link>https://trid.trb.org/View/2632120</link>
      <description><![CDATA[This paper presents a deep reinforcement learning based adaptive traffic signal control framework that explicitly models jaywalking at urban intersections. We integrate a behaviourally grounded Jaywalking Decision model, which endogenises red light violations through waiting time and dynamic gap acceptance, with a Branching Double Deep Q-Network and a comprehensive hybrid action space that controls both phase selection and subphase timing. A multiobjective reward balances delay and jaywalking related safety risk, enabling the controller to respond to non-compliance as it emerges. The framework is evaluated in a multimodal microsimulation of a real intersection in Melbourne across four naturalistic demand scenarios and against both actuated control and established reinforcement learning baselines. Results show that the proposed approach reduces observed pedestrian delay and jaywalking relative to actuated control while achieving a more balanced safety and efficiency profile than single objective or alternative learning architectures. The analysis highlights context-dependent trade-offs that are relevant for policy, since the controller can adapt timing to mitigate non-compliance without assuming full pedestrian obedience. The contributions are threefold: a realistic jaywalking model linked to observable states, a high-granularity action space for multimodal control, and an integrated learning framework that jointly manages delay and safety risk. The proposed framework not only facilitates a more equitable traffic operation system but also offers the first scalable approach to managing risky behaviours in urban traffic environments.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:56:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632120</guid>
    </item>
    <item>
      <title>The effect of a platform management intervention on the behaviour of passengers: A case study at Lund Central station</title>
      <link>https://trid.trb.org/View/2625902</link>
      <description><![CDATA[One of the major causes of dwell time delays is the behaviour of passengers during the alighting and boarding processes, such as an uneven spread of boarding passengers and queues formed in front of the doors. Therefore, changing the behaviour of passengers so that it does not negatively affect the time needed for alighting and boarding is one of the ways to reduce the risk of dwell time delays. Platform management interventions have the potential to induce such a behavioural change, but the real-world impact is not well studied. To fill this gap, the study presented here investigates the effects of a sticker-based platform intervention using video observations from several hundred trains halting at Lund Central Station. We find that the alighting flow rates slightly increased under intervention conditions, with results suggesting that this is due to fewer overlaps in alighting and boarding passenger flows. No statistically significant effects of the intervention on the spread of boarding passengers were found. Our results suggest that changing the behaviour of passengers is likely to be a slow process, requiring additional efforts such as information provision and ensuring that the halting position of a train accurately reflects the information provided by an intervention.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625902</guid>
    </item>
    <item>
      <title>Optimization of Peak-Period Passenger Flow Control and Train Operation on Suburban Metro Line</title>
      <link>https://trid.trb.org/View/2613213</link>
      <description><![CDATA[In order to alleviate the concentrated effect of passenger stranding during peak period on suburban metro lines, an optimisation strategy combining passenger flow control and train operation adjustment is proposed. Considering the constraints of train schedule, train hopping operation, and passenger flow projection, a nonlinear mixed-integer planning model is established to minimize the passenger travel delay time, the number of delays, and the number of train stops, and a hybrid adaptive large neighbourhood search algorithm and a particle swarm algorithm are designed to solve the problem. Finally, the experimental analysis is carried out on the Batong Line of the Beijing Metro, and the experimental results show that, in the case of oversaturation and unbalanced distribution of passenger demand in the peak period, adopting the co-optimisation strategy of passenger flow control and train operation, the total delay time of passenger travel is reduced by 24.1%, and the total number of delay times of passenger travel is reduced by 23.7%.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613213</guid>
    </item>
    <item>
      <title>Passenger centred Transfer Coordination in disturbed Public Transport using Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2627543</link>
      <description><![CDATA[Public transport has proven as an efficient and ecological solution to provide mobility to a large part of the society. Disturbances can impede public transport operation and reduce user acceptance. Current disturbance management strategies primary focus on minimising impacts on operational planning deeming passengers’ needs less important. As a development step towards passenger-centred intermodal disturbance management system, we present a minimal reinforcement learning example for transfer coordination. The environment builds up on the microscopic traffic simulator SUMO. While we consider reinforcement learning as a valuable approach to find novel solutions to passenger-centred disturbance management in larger setups, the goal of this work is to demonstrate the applicability of this method. To this end, it is applied in a small public transport network, demonstrating comparable performance to that of a deterministic transfer coordination strategy.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627543</guid>
    </item>
    <item>
      <title>Predicting public transport delay times from Real-Time GTFS data</title>
      <link>https://trid.trb.org/View/2627525</link>
      <description><![CDATA[In the rapidly evolving field of intelligent transport systems, real-time data plays a crucial role in improving the efficiency and reliability of public transportation. This paper presents a predictive modeling to forecast public transport delays from Real-Time General Transit Feed Specification (GTFS-RT) data provided by Zagrebački Električni Tramvaj. To manage real-time predictions, a web application has been developed that processes, displays, and stores both static and real-time data. Additionally, it enables users to track the current locations of vehicles and schedules, as well as delay predictions per stops. The results indicate a mean absolute delay percentage prediction error of up to 20% for routes with sparse data, and around 10% for routes with dense data. Findings underscore the potential of integrating intelligent transport systems with predictive analytics to enhance public transit services and user experiences in rapidly growing urban areas, contributing to more efficient urban mobility solutions.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627525</guid>
    </item>
    <item>
      <title>Machine learning-based bicycle delay estimation at signalized intersections using sparse GPS data and traffic control signals - A Dutch case study using random forest algorithm</title>
      <link>https://trid.trb.org/View/2633679</link>
      <description><![CDATA[Bicycle delay is an important variable to assess the performance of the cycling transportation system, especially as an indicator of intersection efficiency. This article estimates a machine learning (ML)-based model for estimating average bicycle delays at signalized intersections. This study evaluates various ML models with regressor features, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks. Sparse GPS cycling data (as reference data) from the Talking Bikes program in the Netherlands and the local control signal and flow detection information from the VLOG data provided by a Dutch city are adopted to train the ML models. The findings illustrate the viability of estimating bicycle delays by considering the interplay among weather conditions, temporal factors, junction topology, and local traffic conditions. The estimation model fit using the best-performing model - random forest - has doubled compared to the case without such additional traffic information, indicating its improved performance. Insights gained from the estimation model emphasize the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633679</guid>
    </item>
    <item>
      <title>Adaptive Battery Swapping for Autonomous Delivery Vehicles Using Dueling Double Deep Q Network</title>
      <link>https://trid.trb.org/View/2652214</link>
      <description><![CDATA[Faced with the rapid growth in demand for instant delivery, traditional logistics delivery modes have struggled to meet these needs effectively because of capacity constraints. Autonomous delivery vehicles (ADVs) can compensate for a shortage of human labor. ADVs, which rely on batteries for propulsion, occasionally need to return to battery-swapping stations to maintain the state of charge of their batteries during delivery. In the context of applying ADVs for instant delivery, we employ agent-based modeling to set the behavioral rules of customers, the ADVs, and the distribution center; therefore, an instant delivery scheduling simulation environment is created. A vehicle routing problem with time windows mathematical model is established and solved to optimize the delivery scheduling by the adaptive large neighborhood search heuristic algorithm. Given the dynamically changing environmental conditions, we utilize the Dueling Double Deep Q Network deep reinforcement learning algorithm, which adapts to these changes, to train ADVs on autonomous battery swapping decisions. The performance of the proposed model is compared with several benchmark policies, including threshold-based strategies, alternative reinforcement learning algorithms, and a fixed strategy in which the ADV swaps its battery on each return to the distribution center. Simulation experiments, based on real-world cases, demonstrate that the proposed model achieves better results. Specifically, it reduces the delay time by approximately 17.55% compared with the average delays of all other benchmark policies and decreases the number of battery swaps by approximately 49.06%. Furthermore, the model exhibits strong adaptability to the dynamically changing simulation environment.]]></description>
      <pubDate>Tue, 20 Jan 2026 10:11:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652214</guid>
    </item>
    <item>
      <title>Train dwell time models for crowded metro stations using a bivariate distribution function</title>
      <link>https://trid.trb.org/View/2590623</link>
      <description><![CDATA[Train dwell times at high-passenger-volume stations are complex and inconsistent due to variations in passenger behaviour and interactions. While several studies have examined factors affecting dwell time and developed models to predict it, these models often struggle to accurately predict dwell times under high passenger volume conditions. This poses significant challenges to planning effective timetables in crowded environments. Given this variability, using probability-based approaches to predict dwell time delay could provide better planning. Although some studies have identified dwell time probability distribution functions, they generally do not include passenger volume level as a variable, limiting their applicability in high-density stations.This paper investigates actual operational data to present the limitations of predicting dwell times at high-passenger-volume stations. To address the gap, this paper proposes a bivariate probability function that incorporates passenger volume as a key variable. This gives us a more reliable framework for predicting dwell time delays in crowded environments. The Kolmogorov-Smirnov (K-S) test is used to validate the bivariate dwell time function. This shows the function's capability to predict the probability of achieving target dwell times, which is essential for planning dwell times. Furthermore, this model can be applied alongside delay impact assessments, facilitating a further risk evaluation framework that can be used to make more informed decisions when setting dwell times in timetables.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590623</guid>
    </item>
    <item>
      <title>Research on Two-Stage Pedestrian Crossing Inductive Signal Control Strategy for Autonomous Intersection</title>
      <link>https://trid.trb.org/View/2582660</link>
      <description><![CDATA[Autonomous intersection management (AIM) at “signal-free” intersections under the fully Connected-Automated Vehicle (CAV) environment has become a hotspot. However, few studies show how pedestrians can cross the intersection safely with CAVs. This paper proposes a novel inductive signal control framework considering both pedestrian and CAV demands. This framework consists of two steps. In the first step, a two-stage pedestrian crossing inductive control module for autonomous signal intersections is implemented. In the second step, the CAVs’ trajectories and pedestrian crossing phases are optimised cooperatively. A Mixed Integer Linear Program (MILP) based on conflict-separation is proposed to simultaneously optimise the pedestrian crossing signal phasing scheme and the entry time for CAVs. The goal is to ensure pedestrian crossing safely while optimizing the approaching trajectories of CAVs at the intersection. Numerical experiments are conducted to evaluate the performance and effectiveness of the proposed method under different traffic scenarios. Results show that the proposed method outperforms the signal control mode for pedestrian crossing in one go in terms of reducing average delay under both under-saturated and over-saturated conditions.]]></description>
      <pubDate>Fri, 19 Sep 2025 16:58:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582660</guid>
    </item>
    <item>
      <title>Impact of Truck Access Restrictions on Freight Operations and Driver Quality of Life in Urban India</title>
      <link>https://trid.trb.org/View/2571582</link>
      <description><![CDATA[Truck access restrictions are a common strategy to manage freight traffic in cities and reduce congestion and pollution. However, these restrictions can also lead to significant operational, increase in costs and negatively impact truck drivers and their quality of life. The research identifies unproductive time caused by truck access restrictions and analyzes its effects on drivers in Delhi, India. Findings reveal significant delays and inefficiencies, particularly due to receiver unavailability and operational constraints. Regulatory factors exacerbate delays, impacting freight vehicles involved in regional deliveries. The study calculates the economic burden of these delays, emphasizing the importance of optimizing delivery schedules, reducing unproductive times, and improving policy coordination. Based on the findings, recommendations are provided for a multi-pronged approach to improve urban freight management. These include promoting collaborative practices, evaluating the impact of regulations, investing in designated loading/unloading hubs, and encouraging the use of technology solutions. By implementing these recommendations, policymakers and industry stakeholders can create a more efficient and cost-effective urban freight system in Delhi and similar urban environments.]]></description>
      <pubDate>Wed, 10 Sep 2025 09:22:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571582</guid>
    </item>
    <item>
      <title>A Novel Enhanced Data-Driven Model-Free Adaptive Control Scheme for Path Tracking of Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2512306</link>
      <description><![CDATA[In this paper, an enhanced model-free adaptive control algorithm considering time delay is proposed for the path tracking problem of autonomous vehicles. First, a path tracking mechanism based on the preview-deviation-yaw angle is proposed, which transforms the path tracking problem into a control problem of the preview-deviation-yaw angle. A novel partial form dynamic linearization (PFDL) technique is then employed to transform the vehicle dynamic models into a discrete-time data model with a time-varying pseudogradient (PG), and the proposed controller (PFDL-EMFAC) is designed based on this data model. Moreover, a compensation mechanism is designed for the system time delay by combining the Smith predictor and tracking differentiator (TD). Notably, implementing the controller does not involve any model information; it is a purely data-driven control method. Furthermore, the convergence of the proposed controller is proven via mathematical analysis. The validity of the proposed controller was validated through CarSim-MATLAB cosimulation, and its applicability was verified via the Ankai HFF6668GEV1 autonomous driving platform on a test road in Hefei, China.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:55:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512306</guid>
    </item>
    <item>
      <title>High resolution bus lane performance evaluation from real time update data</title>
      <link>https://trid.trb.org/View/2567594</link>
      <description><![CDATA[Bus priority measures such as bus lanes are designed to enhance bus performance and increase ridership. Traditionally, benefits have been evaluated at an aggregate level. Newer data sources, however, enable the tracking of micro delays and their relation to detailed bus priority data. Given schedule adjustments for bus priority measures, the authors anticipate minimal impacts on expected delay at the route-segment level, with the primary benefit being reduced delay variability relative to the schedule. This study analyzes real bus arrival data to examine the impact of stop-to-stop route characteristics on marginal delay. The analysis uses pooled, between-, and within- effects panel regression models to predict average and standard deviation of marginal delay for each stop-to-stop segment within rolling windows of 30 arrivals. Independent variables include priority measures, traffic signals, traffic volumes, scheduled travel time, stop-to-stop link length, scheduled travel speed, cross-traffic turns, precipitation, weekends, holidays, and the COVID stringency index. Findings reveal that bus-taxi lanes and bus-HOV lanes reduce marginal delay by 6–7 s per kilometer. While the direct impact on marginal delay is minimal due to schedule adjustments, these lanes significantly reduce the variability of delay, saving 5–20 s of standard deviation of delay per kilometer. The study also highlights the substantial impact of traffic signals and cross-traffic turns on bus performance reliability. These findings support the effectiveness of bus priority measures in improving bus service reliability.]]></description>
      <pubDate>Tue, 22 Jul 2025 14:39:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2567594</guid>
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