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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Exploring the dynamic robustness of metro-bus composite networks considering passenger spatiotemporal heterogeneity and behavior characteristics</title>
      <link>https://trid.trb.org/View/2704322</link>
      <description><![CDATA[Practical experience shows that metro-bus composite networks (MBCNs) are irreplaceable for meeting the travel demands of urban residents. In-depth analyses of MBCN dynamic robustness can reveal the mechanisms driving reliable operations. In this paper, we propose a two-layer metro-bus composite network (TL-MBCN) model that integrates the topological structure with its associated passenger flow dynamic. The propagation of cascading failure in the MBCN is then imitated through a linear load-capacity model by considering passengers’ mode choice behavior and rerouting behavior. Three indicators are introduced to assess the dynamic robustness of the MBCN from three perspectives: connectivity, travel time, and passenger flow. Using traffic data from Nanjing, our experiments reveal that (1) the dynamic robustness of the MBCN varies significantly across different periods of the day, and the scale of the cascading failures is strongly correlated with the passenger flow distribution around the attacked station; (2) the dynamic robustness of the network is significantly influenced by the station capacity, passengers’ tolerance threshold, and their prior knowledge of station failures; and (3) the MBCN is more robust under the proposed passenger flow transfer rule.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704322</guid>
    </item>
    <item>
      <title>A Pilot Study for the Service Level of the Metro Station Renovation Project Based on Passenger Detection and Simulation Techniques</title>
      <link>https://trid.trb.org/View/2698293</link>
      <description><![CDATA[With the rapid expansion of urban rail transit networks, the existing metro stations on older lines require renovation and upgrades when integrated with new line stations to accommodate additional transfer functionalities. This poses new challenges for the station’s spatial layout and passenger flow management. This study proposed a comprehensive technical system that leverages YOLO (you only look once) object detection technology in the operation of existing stations, coupled with AnyLogic simulation, to assess the impact of new line stations on passenger flow within old-line stations. The system was designed to predict congestion levels at old-line stations after they were upgraded to transfer stations. Passenger flow dynamics can be accurately monitored by collecting and analysing real-time data using YOLOv8 at the key nodes of existing stations. This approach can help us analyse the effective reference indicators to choose the best congestion mitigation plan. The simulation results provide a scientific basis for predicting and alleviating congestion at key nodes during the station renovation process, offering valuable references for ensuring passenger safety and enhancing the station throughput capacity.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:23:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698293</guid>
    </item>
    <item>
      <title>Optimizing timetabling and platoon formation of shuttle transit with modular autonomous vehicles considering time-dependent passenger demand</title>
      <link>https://trid.trb.org/View/2698422</link>
      <description><![CDATA[Traditional public transit with fixed vehicle capacity often leads to underutilization, especially during off-peak hours. Modular autonomous vehicles (MAVs) enable flexible capacity through coupling and decoupling. This study jointly optimizes timetabling and platoon formation for an MAV-based shuttle system under time-dependent demand. A bi-objective model balances operator and passenger costs, solved via the ε-constraint method to obtain Pareto-optimal solutions. A case study confirms the model’s effectiveness, demonstrating that the MAV system reduces total operating costs and improves service—achieving a 73.70% decrease in empty seat time and a 29.75% reduction in passenger waiting time compared to traditional fixed-capacity transit.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:23:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698422</guid>
    </item>
    <item>
      <title>Formulating the express railway network design for relieving in-vehicle crowding, incorporating a bi-level modeling approach</title>
      <link>https://trid.trb.org/View/2698417</link>
      <description><![CDATA[Crowding in vehicles is worse in concentrated urban areas. It is necessary to extend the public transit network, delivering travelers with a high level of service. This study aims to develop a design model of an express railway to improve mobility and relieve congestion on the existing urban rail transit network. A bi-level structure has been employed to solve the network design problem. The upper-level model is formulated to maximize the net-benefit of the entire rail network, whereas the lower-level model is formulated to grasp the effect of crowding on vehicles. A genetic algorithm is employed to solve the combinatorial optimization problem in a reasonable time. The results show that the developed model can find the optimal express railway by reflecting the relation of the trade-off between accessibility and mobility of the system. This study contributes to the establishment of the long-term plan for public transit networks in metropolitan areas.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:23:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698417</guid>
    </item>
    <item>
      <title>Optimization Strategies for Transportation Network Resources in the Context of Intercity Commuting: A Case Study of the Beijing–Tianjin Intercity Railway in China</title>
      <link>https://trid.trb.org/View/2697818</link>
      <description><![CDATA[With the steady rise of intercity commuting demand, major transportation hubs are experiencing increasing operational pressure and pronounced imbalances in passenger flows. In many metropolitan areas, passenger demand is overly concentrated at principal stations, leading to congestion and underutilization of newly added stops within the same metropolitan network under the current timetable. To mitigate these imbalances and improve overall service coordination, this study proposes a dynamic coupling optimization model that simultaneously determines train stopping patterns and formation plans under a multiperiod coordination framework. The model integrates time-varying passenger demand, operational costs, schedule deviation penalties, and transfer coordination effects into a unified objective to achieve a coordinated match between supply and demand. A heuristic algorithm is developed and applied to the Beijing–Tianjin Intercity Railway, with Yizhuang Station as the case study. Results indicate that deploying larger train formations during the morning peak can effectively increase line capacity and create conditions for additional stops at Yizhuang Station. Compared with single-dimensional stop optimization, the proposed model improves network efficiency and passenger accessibility while minimizing timetable adjustments. Furthermore, by quantifying the impact of fare levels on destination choice behavior, the study demonstrates that coordinated fare design can further promote balanced intercity passenger flows.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697818</guid>
    </item>
    <item>
      <title>Modelling the Distribution of Passenger Traffic on Marching Routes with a Combined Mode of Movement</title>
      <link>https://trid.trb.org/View/2579779</link>
      <description><![CDATA[The paper establishes the theoretical prerequisites for studying the methods of organising traffic on urban and suburban routes. Different approaches to planning the route network are considered, including optimisation of traffic schedules, balancing the load on different types of transport and ensuring the convenience of transfers. Particular attention is paid to the analysis of passenger traffic on the studied routes. Based on a detailed analysis of urban passenger transport systems, key performance criteria such as efficiency, environmental friendliness, travel time and comfort were identified. The conducted research allowed to evaluate the motivational factors influencing the choice of transport connection by passengers, which made it possible to build a model of passenger flow distribution on routes with a combined mode of movement. The results of the study can be used not only to optimise existing routes, but also to create new transport models for cities, taking into account future trends in infrastructure development and increased demand for public transport. The study also takes into account the impact of modern technologies on passenger transport management and the improvement of the quality of transport services. The recommendations provided can also be used to develop strategies for the development of large cities, namely the introduction of modern information technologies for traffic management, the development of electric transport and the introduction of smart transport systems that automate data collection and traffic control.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579779</guid>
    </item>
    <item>
      <title>Bilevel transit timetabling with synchronization at origin and transfer stops</title>
      <link>https://trid.trb.org/View/2694780</link>
      <description><![CDATA[Passenger waiting times at both origin and transfer stops significantly impact the attractiveness of public transit. While transfer coordination has received considerable attention, initial waiting times at origin stops are relatively overlooked, despite affecting all passengers. This paper introduces a bilevel transit timetabling framework that jointly optimizes synchronization at both origin and transfer stops to minimize total passenger travel cost. The model incorporates continuous passenger arrival distributions to improve the accuracy of initial waiting time estimation, time-dependent inter-stop travel times to reflect operational variability, and vehicle capacity constraints that influence actual boarding times. Passenger route choices are modeled as a network equilibrium, capturing how boarding decisions interact with timetable design. The resulting bilevel problem is solved using a customized gradient projection algorithm, which leverages the Augmented Lagrangian method to handle capacity constraints, Mirror Descent to efficiently compute passenger equilibrium flows, and Automatic Differentiation to obtain accurate gradients for timetable optimization. Numerical experiments on a synthetic network and the Sioux Falls network demonstrate that the proposed approach improves timetable coordination and significantly reduces passenger waiting times across the network.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694780</guid>
    </item>
    <item>
      <title>Does airport connectivity increase passenger traffic? Evidence by airline type in China</title>
      <link>https://trid.trb.org/View/2691081</link>
      <description><![CDATA[Enhanced airport connectivity is widely linked to passenger traffic growth, yet conditions under which this relationship holds remain unclear. This study analyzes how connectivity shaped by different airline network structures affects airport passenger volumes in China’s domestic market from 2009 to 2019. Using a two-way fixed effects panel model, we evaluate how these effects vary by airport scale and geographic location. Results show that the impact of connectivity is not uniform: accounting for differences in airport operational scale, the impact of connectivity on passenger traffic at 𝘊𝘰𝘳𝘦 and 𝘎𝘳𝘰𝘸𝘪𝘯𝘨 airports varies across airline network types. When spatial heterogeneity is considered, the effect of connectivity on airport passenger traffic across regions likewise varies by airline network type. The findings highlight that both airport characteristics and airline network types condition the benefits of connectivity, offering guidance for more targeted and efficient aviation subsidy policies.]]></description>
      <pubDate>Mon, 13 Jul 2026 10:45:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691081</guid>
    </item>
    <item>
      <title>Joint Optimization of Passenger Flow Control and Train Skip-Stopping for Overcrowded Metro Lines: A Multi-Agent Reinforcement Learning Approach</title>
      <link>https://trid.trb.org/View/2663055</link>
      <description><![CDATA[During rush hours, the capacity of metro in megacities is insufficient to meet the travel demand, resulting in oversaturation and high risk on platform in stations, especially transfer stations. This paper addresses this problem through the joint optimization of some operational interventions, aiming to alleviate passenger overloads while maintaining travel efficiency. To make the model more realistic, the stochastic characteristics of passengers are considered, including the probability distribution of passenger arrival time, inbound and transfer walking times. To provide a high-quality solution for the complex constraint model, three cooperative agents—governing passenger inflow, transfer flows, and train skip-stopping mode—are architected within improved Double Deep Q learning Network (IDDQN) to form a multi-agent reinforcement learning solution. Empirical validation on Beijing Metro Line 13 and Changping Line demonstrates that the multi-agent framework proposed in this paper can eliminate 100% of passenger over-limit flow while reducing the average waiting time of passengers. It also has a significant improvement in reducing stochastic characteristic impact and accelerating convergence.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663055</guid>
    </item>
    <item>
      <title>Cascading failure analysis for multimodal comprehensive transportation network considering time-delay characteristic of passenger flow redistribution</title>
      <link>https://trid.trb.org/View/2688673</link>
      <description><![CDATA[Under external disturbances, considering passenger flow transfer time – referred to as the time-delay characteristic – rather than redistributing the passenger flow instantaneously, is crucial for analyzing cascading failures in multimodal comprehensive transportation network (MCNet). This study proposes an improved cascading failure model that incorporates passenger flow transfer time, transfer paths, and multiple transfer modes. A systematic analytical method is designed to examine cascading failure characteristics under different influencing factors in MCNet. The experimental results reveal that attacking the highest-strength vertex triggers a more severe cascading failure. Besides, the cascading failure in MCNet is primarily induced by passenger flow rather than network structure, while the time-delay characteristic can slow down the failure propagation by 43.6%. Moreover, an increasing proportion of volume transferred within the same mode can mitigate the cascading effects. Finally, based on the research findings, we propose several suggestions for network design and optimization to enhance the resilience of MCNet.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688673</guid>
    </item>
    <item>
      <title>Origin-destination flow generation for metro network expansion using spatiotemporal gated graph neural networks</title>
      <link>https://trid.trb.org/View/2689820</link>
      <description><![CDATA[Rapid urbanization drives continuous expansion of metro networks to meet rising mobility demand. Due to the large investment, such expansion requires careful planning, including detailed assessment of potential Origin-Destination (OD) passenger flows. However, forecasting OD flows becomes particularly challenging in evolving metro networks; new stations and lines introduce OD pairs without historical ridership data, while travel patterns shift as passengers adapt to network changes. Traditional gravity-based models rely on oversimplified assumptions and overlook complex network dependencies within metro systems. Meanwhile, recent deep learning methods designed for regional-scale mobility often struggle to capture the fine-grained dynamics and structural evolution unique to metro networks. To address these limitations, we propose a Spatiotemporal Gated Graph Neural Network (STG-GNN) to address the OD flow generation for metro network expansion problem. STG-GNN integrates diverse urban contextual data and dynamically evolving metro network structures. Spatial dependencies are modeled using graph attention networks built on both transfer-aware travel time and geographic distance, while temporal patterns are captured via Gated Recurrent Units (GRUs). A novel gated fusion module adaptively combines spatial and temporal outputs, weighting their contributions based on OD pair characteristics. Additionally, an age-aware weighted loss function is used to reflect the maturation process of new OD pairs. We validate STG-GNN with extensive experiments using multi-year metro ridership data from Shenzhen, China. Results show that STG-GNN consistently outperforms existing state-of-the-art models, improving Common Part of Commute (CPC) by 13.6% and reducing RMSE and MAE by 11.3% and 4.7% for new OD pairs. The proposed model is general and can be adapted to other evolving transportation networks. The source code and a synthetic dataset are made publicly available for transparency and reproducibility.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689820</guid>
    </item>
    <item>
      <title>Integrated Timetabling and Scheduling of Modular Autonomous Vehicles under Uncertainty</title>
      <link>https://trid.trb.org/View/2686226</link>
      <description><![CDATA[Addressing the integrated timetabling and vehicle scheduling (TTVS) problem is important for improving transit operations. Recently, the emerging modular autonomous vehicles composed of modular autonomous units have made it possible to dynamically adjust onboard capacity to better match space-time imbalanced passenger flows. This paper introduces an integrated framework for the TTVS problem in a dynamically capacitated and modularized bus network considering time-varying and uncertain passenger demand. In this network, units can be (de-)coupled and rerouted across different lines within the network at various times and locations, providing passengers with the opportunity to make in-vehicle transfers—that is, to transfer between lines while remaining on board. We formulate a stochastic programming model to jointly determine the optimal robust timetable, dynamic formations of vehicles, and cross-line circulations of units, aiming to minimize the weighted sum of operators’ and passengers’ costs. To solve realistic instances, we propose a tailored integer L-shaped method to solve the formulated model dynamically through a rolling-horizon (RH) optimization algorithm. Furthermore, we extend our approach into a novel learning-based real-time decision-making framework that fine-tunes timetables and reoptimizes vehicle schedules in response to evolving and new demand realizations during practical operations. At its core is a scenario-retention method that selects a representative subset of scenarios using a machine learning model trained on scenario-level features. This subset is then incorporated into the optimization, ensuring both computational scalability and solution quality. To validate the effectiveness of our methods on realistic instances, we conduct experiments based on the Beijing bus network involving two bidirectional lines, 89 stops, up to 50 trips, and a four-hour operational horizon. Our integrated optimization method outperforms the sequential approach. Compared with fixed-formation vehicles, our approach generates timetables and vehicle schedules that require fewer units. Additionally, the learning-based real-time decision-making framework outperforms benchmark algorithms in solution quality within a one-minute computation time limit.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686226</guid>
    </item>
    <item>
      <title>Belief updating in the low-altitude economy: safety information, presentation context, and willingness to pay</title>
      <link>https://trid.trb.org/View/2681477</link>
      <description><![CDATA[When a new service is introduced, business success hinges on how consumers perceive its risks. This study examines public attitudes toward low-altitude Passenger Transport (PT) and Goods Delivery (GD), focusing on perceived safety, willingness to participate, and willingness to pay (WTP). We field an online survey experiment with a mixed design: a within-subject contrast (Pre-Info → Post-Info) to assess updating after targeted safety statistics, and between-subject factors contrasting Single-project framing (SPF) versus Multi-project framing (MPF) and PT versus GD. Safety information increased perceived safety across all groups, and perceived safety was positively correlated with WTP both Pre-Info and Post-Info, with the association modestly stronger Post-Info. Presentation context also mattered: MPF yielded more favorable WTP distributions than SPF overall, with the strongest gains for GD. For PT, MPF reduced safety concern relative to SPF in Pre-Info comparisons, but its incremental Post-Info effect was small and not statistically significant in regressions. Across conditions, GD was viewed as safer and more acceptable than PT. These findings suggest that framing a new low-altitude service within a broader portfolio and providing concise safety statistics can enhance acceptance—especially for GD—while PT may require additional, service-specific assurances as part of a phased introduction strategy.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:54:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681477</guid>
    </item>
    <item>
      <title>Short-Term Passenger Flow Prediction of Urban Rail Transit Integrating Multiple Features: A Hybrid Model with Two-Stage Feature Selection</title>
      <link>https://trid.trb.org/View/2685633</link>
      <description><![CDATA[Urban rail transit systems encounter challenges in short-term passenger flow prediction due to complex spatiotemporal data and dynamic travel patterns. Traditional models often struggle to capture these complexities. This study introduces a hybrid prediction framework that employs a two-stage feature selection strategy and multiscale decomposition to enhance forecasting accuracy. Key innovations include (1) a two-stage feature selection combining correlation analysis and recursive feature elimination, enabling the identification of the most influential temporal, spatial, and external features; (2) complete ensemble empirical mode decomposition with adaptive noise for isolating multiscale patterns; (3) parallel modeling with modern temporal convolutional networks and extended long short-term memory; and (4) a cross-attention mechanism for spatiotemporal feature fusion. Tested on 22 stations of Fuzhou Rail Transit Line 2 (8 million automatic fare collection records), the proposed model significantly outperforms classical and deep learning baselines. An ablation analysis further validates the contribution of each module to the overall performance. By bridging advanced AI methodologies with practical transit management needs, this work advances scalable, data-driven decision-making for sustainable urban mobility systems.]]></description>
      <pubDate>Tue, 30 Jun 2026 15:52:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685633</guid>
    </item>
    <item>
      <title>Integrated Approach for Convoy Dispatching and Passenger Routing at Railway Stations with Variable Composition Trains</title>
      <link>https://trid.trb.org/View/2580110</link>
      <description><![CDATA[This paper addresses a railway scenario with variable composition trains, and focuses on the management and simulation of the passenger flows and trains at stations. In particular, it proposes a management system aimed at optimally routing the passengers from their entrance in the station to the correct platform segment according the train they have to board, and scheduling the train service in terms of convoy composition, capacity and destinations. The resulting system is then modelled as a discrete event system (DES) and simulated, to evaluate its performance on the basis of indicators like the queue length, the unsatisfied demand, the passenger travel time in the station, and the passenger density at the platform. The proposed station management system is then tested on a mixed real/synthetic numerical example aimed at proving the feasibility and functioning of the proposed approach.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580110</guid>
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