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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Spatial exploration and evaluation of rail and long-distance bus network integration with a multimodal node-place model</title>
      <link>https://trid.trb.org/View/2676356</link>
      <description><![CDATA[Improving the integration of rail and long-distance bus services is essential for enhancing regional connectivity and sustainable transport accessibility. Existing node-place models mainly focus on station surroundings and do not consider how buses extend access explicitly to support the transit network. Therefore, this study develops a multimodal network-based approach applied to a case of sub-rural Scotland. Three new integration metrics are designed — travel time-weighted population (demand coverage), feeder bus service availability (supply), and network centrality (regional connectivity). These indicators are then incorporated into a three-dimensional node-place framework, which evaluates the integration performance of 102 railway stations across the study area. Results reveal spatial differences in integration performance, identifying well-connected hubs such as Inverness and Stirling, alongside stations like Rosyth and Dunfermline City where demand is not matched by service provision. The analysis also shows opportunities to strengthen east–west regional links and improve multimodal access through targeted interventions, such as co-locating bus termini. By extending the node–place model to include multimodal catchments and network-level connectivity, the framework captures aspects of integration that are overlooked in rail-only assessments and offers a unified diagnostic tool for identifying where rail-bus integration improvements may have the greatest effect. The method is built on publicly available data, enabling its application in other regions and adaptation to support future demand modelling.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:17:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676356</guid>
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    <item>
      <title>Secondary level bus network design: new method and case study</title>
      <link>https://trid.trb.org/View/2682082</link>
      <description><![CDATA[We propose a new method to generate a secondary level public transport network, which considers explicitly the interaction with an existing primary level trunk network. While the trunk network operates high speed and capacity services to support high demand corridors, the services of the secondary network are operated by regular buses running over the street network and include both feeder routes that connect to the trunk, and independent routes that serve demand which does not use the trunk network. The proposed method is an extension of an existing heuristic which generates routes based on shortest paths between high demand origin-destination pairs and the insertion of further nodes in existing routes, seeking to obtain solutions which are convenient to both users and operators. Due to the nature of the resolution method, the results are sensitive to the operational speed of the trunk network and to the imposed maximum route length. We apply the proposed methodology to a case study related to a medium-size city, where trunk corridors are planned to be deployed in an incremental way, which poses the need for having a systematic procedure to adjust the complementary secondary level bus network. Experimental results allow us to conclude that a fast trunk network reduces the user total travel time and the operational cost of the secondary bus network. Moreover, it reduces the usage of urban public space and improves its environment, by reducing the size of the operating fleet. This work provides a preliminary method to solve a relevant planning problem and formulates some conclusions regarding the relationship between the primary and the secondary public transportation networks. Finally, we identify some extensions to improve the proposed method toward its application to real settings.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682082</guid>
    </item>
    <item>
      <title>A novel method for complexity analysis of marine traffic based on complex networks</title>
      <link>https://trid.trb.org/View/2625532</link>
      <description><![CDATA[The identification of marine traffic complexity is critical for the development and implementation of intelligent maritime transportation systems. Analyzing extensive data on ship movements enhances situational awareness and aids Vessel Traffic Services Operators (VTSOs) in the real-time monitoring of complex ship behaviors in waterways. However, the predominant systems-based analysis of marine traffic predominantly utilizes undirected Marine Traffic Situation Complex Network (MTSCN), which is inconsistent with the actual navigation situation. Firstly, a directed MTSCN is constructed in this study, which accounts for the asymmetry of navigational influences between ships. Secondly, a Node Importance Evolution Model (NIEM) is developed for the directed network of marine traffic, employing two indicators: the comprehensive degree and the comprehensive strength. Finally, the evaluation performance of the NIEM is substantiated through case studies and robustness analysis. The research results show that the construction of the directed MTSCN takes into account the differences in traffic complexity between ships, the evaluation indicators consider the transmission contributions of ship nodes within the network, and therefore fits the actual nautical situation better than the undirected MTSCN. The findings confirm that the newly developed model significantly aids VTSOs in identifying high-complexity ships requiring closer supervision, thereby enhancing marine traffic management and improving maritime safety.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:59:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625532</guid>
    </item>
    <item>
      <title>Port resilience assessment and analysis with dynamic cargo coordination in network: The case of China's global liner shipping</title>
      <link>https://trid.trb.org/View/2681358</link>
      <description><![CDATA[Frequent port disruptions and congestion have exerted extensive impacts on shipping networks, rendering port resilience increasingly crucial as it determines port efficiency and network performance. We propose a framework to identify and assess container port resilience from a liner shipping network perspective, focusing on absorptive, adaptive, and recovery capacities. This study develops a novel measurement method that employs a cascading congestion model to simulate network congestion by considering both node structure and functional attributes. The framework designs a downward neighboring redistribution mechanism for ship adaptive decision-making, and quantifies resilience performance by capturing port structure changes and network damage during congestion resistance. It is applied to China's pivotal global liner shipping network. The results reveal that small and medium-sized Chinese ports in the Pearl River Delta and other regions exhibit strong recovery capacities, attributable to port group integration. Approximately 77% of ports exhibit uneven resilience development. Super-large ports display a positive correlation between their adaptability and control capability, but demonstrate weaker recovery, suggesting a need for enhancement. Among China's trade partners, Vietnam, Australia and South Korea ports exhibit strong resilience, Turkey and Brazil show strong adaptability. This suggests potential for risk dispersion through coordinated operations with neighboring ports. European Union ports maintain balanced development across all resilience capacities. This study contributes to the exploration of resilience characteristics in ports and shipping networks, providing insights for resilient operational decision-making for ports and ships, port optimization in resilient route planning, and the interplay between global trade cooperation and ports/shipping routes operational management.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681358</guid>
    </item>
    <item>
      <title>Learning heuristics for transit network design and improvement with deep reinforcement learning</title>
      <link>https://trid.trb.org/View/2643283</link>
      <description><![CDATA[Planning a network of public transit routes is a challenging optimisation problem. Metaheuristic algorithms search through the space of possible transit networks by applying heuristics that randomly alter routes in a network. Existing algorithms almost exclusively use heuristics that modify the network in purely random ways. In this work, we explore whether we can obtain better transit networks using more intelligent heuristics, that modify networks according to a learned preference function instead of at random. We use reinforcement learning to train graph neural nets to act as heuristics. These neural heuristics yield improved results on benchmark synthetic cities with 70 nodes or more, and achieve new state-of-the-art results on the challenging Mumford benchmark. They also improve upon a simulation of the real transit network in the city of Laval, Canada, achieving cost savings of up to 19% over the city's existing transit network.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643283</guid>
    </item>
    <item>
      <title>Vehicle routing problem with en-route delivery</title>
      <link>https://trid.trb.org/View/2643243</link>
      <description><![CDATA[Traditional last-mile delivery usually requires customers to offer predetermined locations for receiving parcels. However, the advancement of information and communication technology enables the collection and utilization of real-time information for more innovative, flexible, and cost-saving ways of delivery. This paper introduces a new last-mile delivery problem where en-route deliveries can occur at any node or arc along customers’ trajectories. Customers’ trajectories are first converted into candidate time windows across nodes and arcs, with exactly one time window per customer required to enable delivery. This problem formulates a general routing problem that minimizes total travel cost, which is then transformed into a node-routing problem solvable through mixed-integer linear programming. Results show that flexible en-route deliveries significantly reduce transportation costs compared to traditional delivery approaches where delivery can be made at only one location. Moreover, en-route delivery is particularly effective when service time at arcs is shorter than at nodes.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643243</guid>
    </item>
    <item>
      <title>Node Criticality Estimation in Intermodal Networks</title>
      <link>https://trid.trb.org/View/2665887</link>
      <description><![CDATA[The Critical Node Problem (CNP) is a network optimisation challenge focused on identifying a subset of nodes whose removal most significantly impacts network connectivity. This problem has broad applications in areas such as network security, infrastructure resilience, and social network analysis. Its complexity arises from the need to evaluate the influence of node removals on network structure under various constraints and multiple objectives. This study introduces a novel node criticality estimation framework for intermodal networks, integrating centrality measures with congestion-aware freight redistribution. The proposed method combines graph-theoretical indicators (degree, betweenness, closeness) with operational data from an intermodal transhipment problem that accounts for terminal capacities, transport costs and emissions. This integration enables a more realistic assessment of node importance based on both topological and flow-based impacts. A comparative analysis demonstrates the advantages of this model in terms of faster operational recovery of the disrupted network. Using a case study of a European freight transport network, the closure of a single node is simulated through a subnetwork to demonstrate its high impact on the overall network’s efficiency and flow resilience. It addresses the following questions: which node in the network is the most significant? What is the geographical extent of the disruption of that node? The results highlight the importance of highly connected terminals, intermodal transfer points, and their role in maintaining networks’ efficiency.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665887</guid>
    </item>
    <item>
      <title>Efficient Method for Steady-State Simulation of Natural Gas Pipeline Networks Based on Spatial Decomposition</title>
      <link>https://trid.trb.org/View/2643062</link>
      <description><![CDATA[As the scale of natural gas pipeline networks expands annually, their topological structures become increasingly complex, leading to a continuous increase in the difficulty and time cost of solving pipeline network simulation problems. Addressing the technical challenge of balancing accuracy and timeliness in natural gas pipeline network simulation, a new method for accelerating the solution of natural gas pipeline network simulations based on spatial decomposition and parallel computing is proposed, under the condition of clarifying the principles for determining simulation boundary conditions. In this study, the core issue is the steady-state hydraulic simulation of natural gas pipeline networks, which is transformed into an unconstrained optimization problem. Based on the hydraulic simulation equations, the objective function of the corresponding optimization model is constructed, transforming the solution of the simulation model into an extreme value optimization process of the optimization model. Thus, an optimization-based solution model for steady-state simulation of natural gas pipeline networks is established. By conducting a partitioned demonstration of the mathematical characteristics of the objective function and the properties of extreme points, the prerequisites for the uniqueness of the solution are clarified, providing rigorous mathematical theoretical support for model simplification through spatial decomposition and partitioned decoupling. Combined with a case study of pipeline network simulation, the conclusion is further proven: when solving the steady-state simulation problem of natural gas pipeline networks, under the condition of at least one given node pressure, the solution to the steady-state hydraulic calculation of the pipeline network is unique. Meanwhile, the results of simulation calculations using spatial decomposition and parallel acceleration (SDPA) for natural gas pipeline networks with complex topological structures show that, compared to TGNET result data, the average error in pressure is 0.10%, and the average error in flow rate is 1.25%. Under the premise of ensuring the calculation accuracy of the solution, the calculation time is reduced by 59.65%.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:01:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643062</guid>
    </item>
    <item>
      <title>Study on the synergistic development efficiency of Guangzhou under the rail transit networks: A dual-dimensional perspective of inter-district and inter-industry</title>
      <link>https://trid.trb.org/View/2673315</link>
      <description><![CDATA[In recent years, promoting regional synergistic development has become a key objective for the next stage of global city development. The rapid construction and continuous improvement of rail transit networks (RTNs) have significantly reshaped urban spatial organization and the flow of resources, profoundly affecting synergistic development. This study aims to enrich the theoretical and empirical literature on regional coordinated development in China at a micro scale, taking Guangzhou—a major economic hub in China—as the research object. Under the context of RTNs, the authors employ quantitative modeling and big data analysis to measure synergistic development levels along two dimensions: inter-district and inter-industry. The evolution of Guangzhou’s synergistic development is further analyzed. Results show that Guangzhou’s overall development level continues to improve, inter-district disparities exhibit dynamic convergence, and the overall synergistic development level is continuously optimized. RTN connectivity and agglomeration levels have significantly increased, while cross-district rail connections still require enhancement. Districts such as Yuexiu and Liwan serve as core nodes, whereas Nansha has not yet assumed a sub-central city role. The spatial layouts of manufacturing and productive service industries (PSIs) show considerable similarity; the spatial similarity is strongest between transportation service industries and manufacturing, followed by science and technology(S&T) services.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673315</guid>
    </item>
    <item>
      <title>Supply chain integration through network optimisation to facilitate dynamic entity addition for a wild silk industry</title>
      <link>https://trid.trb.org/View/2639337</link>
      <description><![CDATA[This article aims to achieve integration in the supply chain of Muga silk by optimising the transportation distance in the supply chain network. The objectives of this study include re-modelling the existing supply chain to integrate identified entities besides facilitating the dynamic addition of future entities without causing disruptions. The research follows an experimental design and uses network analysis techniques to present an optimal network that seeks to achieve supply chain integration by an incremental selection of the nearest node while minimising transportation distance in the overall network. Findings from the study can facilitate the creation of closed-looped ecosystems already existing in different districts and integrate them into the greater supply chain of the Muga silk industry. This process can be used to achieve similar entity collaboration in neighbouring districts, resulting in improved logistics in the industry.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:49:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639337</guid>
    </item>
    <item>
      <title>Bike-sharing ridership prediction for network expansion using graph neural networks</title>
      <link>https://trid.trb.org/View/2624214</link>
      <description><![CDATA[Ridership prediction in station-based bike-sharing services improves station planning, fleet management, and network design. Ridership inflow and outflow prediction at the station level has received significant attention through trip production and attraction models. However, station-to-station ridership has been studied less, despite its widespread applications in use cases such as bike-lane planning or fleet electrification. This study introduces a Graph Neural Network (GNN) to model station-to-station ridership using a customized Graph Sample and Aggregate framework to generate node embeddings and minimize the weighted Mean Squared Error for peak periods. The model incorporates the characteristics of the network, sociodemographic features, and station properties. We present the case study of Bikeshare Toronto to train and test the GNN model and benchmark it against other standard prediction methods. We show that the GNN outperforms linear regression, spatial regression, XGBoost, and artificial neural networks due to its ability to capture the impact of the network structure on ridership patterns. We incorporate the GNN model in five design scenarios focusing on urban core connectivity, suburban access, transit integration, equitable accessibility, and tourist hubs. Each scenario is strategically developed to prioritize and address unique urban challenges. To enhance the model’s application in real-world planning, we embedded the model in a web-based tool for the Cities of Vancouver and Toronto, allowing for further “what-if” scenario analysis in bike-sharing network planning.]]></description>
      <pubDate>Mon, 23 Feb 2026 11:23:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624214</guid>
    </item>
    <item>
      <title>Optimization on Multimodal Network Considering Time Window Under Uncertain Demand</title>
      <link>https://trid.trb.org/View/2591236</link>
      <description><![CDATA[Improving transport efficiency is challenging for multimodal transport participants to improve cost-effectiveness. This paper proposes to select city nodes and establish a multi-objective fuzzy optimization model with mixed time window constraints to consider customer demand and transportation time uncertainty. T-rex Optimization algorithm (TROA) is used to solve the problem, which efficiently lowers transportation costs and carbon emissions and has higher precision and dependability than Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The efficacy of this method is proven using the example of the multimodal transportation network in China’s central-eastern economic zone. These findings provide potential solutions for multimodal transportation aimed at enhancing transportation efficiency.]]></description>
      <pubDate>Thu, 19 Feb 2026 17:02:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591236</guid>
    </item>
    <item>
      <title>Multi-Authority CP-ABE Scheme With Cryptographic Reverse Firewalls for Internet of Vehicles</title>
      <link>https://trid.trb.org/View/2553369</link>
      <description><![CDATA[Internet of vehicles, featured with widely distributed vehicle nodes and limited computing power, usually have high performance requirements. Because of this feature, efficient and reliable access control has raised a challenge in Internet of vehicles. Ciphertext-policy attribute-based encryption (CP-ABE) could be denoted as an efficient solution for this problem. However, directly applying traditional single-authority CP-ABE schemes may result in single-point performance bottleneck. Besides, the secrets of the whole system may be leaked if any node is attacked. To solve these challenging tasks, we proposed MA-CP-ABE-CRF, a multi-authority CP-ABE scheme with cryptographic reverse firewalls. The system is designed to grant vehicles fine-grained access control by encrypting data under vehicle attributes. Besides, load balancing of authorization in distributed systems is achieved based on the characteristic of multi-authority. Meanwhile, specific nodes are equipped with cryptographic reverse firewalls (CRFs) to prevent information leakage. As the first scheme with the above features for Internet of vehicles, the system achieves adaptive CPA-security and ASA-security. Through rigorous theoretical analysis and experimental comparison, MA-CP-ABE-CRF is proved to be highly efficient and practical.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553369</guid>
    </item>
    <item>
      <title>Integrating graph and neural relational inference for network-wide train delay prediction: An equilibrium between accuracy and interpretability</title>
      <link>https://trid.trb.org/View/2622403</link>
      <description><![CDATA[Accurate train delay predictions contribute to real-time decision-making, and comprehending the intricate interactions among diverse elements of delay evolution holds paramount significance for tactical timetabling. However, existing research struggles to strike an equilibrium between the accuracy and interpretability of delay prediction models. This paper introduces NRI-GraphSAGE, a predictive model for railway network delay evolution, successfully harmonizing interpretability and accuracy by integrating neural relational inference (NRI) and Graph Neural Networks (GNNs). The proposed model follows a standard encoder-decoder structure. The model’s encoder module employs a variational autoencoder structure to learn train-train interactions. In the model’s decoder module, heterogeneous GNNs are used to process the acquired train-train interactions and other information guided by domain knowledge. Case studies on two local networks of the Chinese high-speed railway affirm the rationality of each module within NRI-GraphSAGE and showcase its outstanding predictive accuracy. Through experiments, we affirm the significance of interactions between elements (station-train, disturbance-train, station-station) in the railway network, alongside the sensitivity of influencing features. Furthermore, an analysis of the learned train-train interactions reveals that multiple adjacent trains can interact, and the strength of interactions increases with the decrease of headways or growth of train delays. Compared with existing approaches that rely on predefined relationships, our model automatically infers these interactions from historical data, more accurately capturing critical train interactions. Consequently, the high predictive accuracy of NRI-GraphSAGE furnishes dispatchers with a foundation for crafting rescheduling decisions, while explaining the interactions of different elements during the delay evolution lends support to the allocation of recovery time in timetable planning.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622403</guid>
    </item>
    <item>
      <title>Worldwide airline network evolution between 2000 and 2023: A systematic analysis of hubs, hub links, allocations, and direct links</title>
      <link>https://trid.trb.org/View/2619181</link>
      <description><![CDATA[This study examines the evolution and structural patterns of hub networks within global air transportation systems from 2000 to 2023. By analyzing key network metrics - such as the number of hubs, hub link density, the number of allocation links per spoke, and direct links bypassing hubs - this research uncovers trends and shifts in airline network configurations over time. The analysis is based on a comprehensive, worldwide air transport dataset spanning over two decades, offering a robust foundation for identifying changes in network design and operational strategies. Full-Service Carriers (FSCs) and Low-Cost Carriers (LCCs) expanded their hubs–especially in North-East Asia–while Regional/Commuter carriers reduced theirs, presenting new logistical and financial challenges. The distinction between FSCs and LCCs has blurred, with both adopting hybrid strategies–FSCs unbundling services and LCCs incorporating premium features–driven by market demands and competitive pressures, ultimately intensifying competition and benefiting consumers. Additionally, the rise of direct links, particularly among LCCs in Western Europe and South-East Asia, reflects a shift toward efficiency and passenger-centric travel, necessitating adaptive infrastructure and sustainable practices to balance profitability and environmental considerations. Finally, our study is the first to provide empirical fits for the magnitude of network elements versus airline type and the size of the network. Finally, this study presents the first empirical quantification of network element magnitudes relative to airline type and network size, offering novel insights into the structural dynamics of global airline operations.]]></description>
      <pubDate>Thu, 12 Feb 2026 08:51:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2619181</guid>
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