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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>
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      <title>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Relationship between Resilience and Topological Structure and Their Application in the Planning of Zhejiang Province Intercity Road Networks</title>
      <link>https://trid.trb.org/View/2658318</link>
      <description><![CDATA[Deteriorating global climate that contributes to increasing external disturbances involving hurricanes, floods, heavy rains, mudslides, landslides, earthquakes, and traffic accidents often causes disruptions in certain roads. However, a road network with high resilience can almost retain its global network efficiency, depending on its effective self-reorganization, even if certain roads are disrupted. By integrating complex network theory into resilient city theory, the study investigates the impact of different road network topologies on road network resilience and proposes strategies for spatial planning for resilient road networks. Firstly, the theoretical definition of road network resilience is proposed; the implications of global network efficiency, network collapse threshold, and network resilience index are expounded; the assessment method for the road network resilience indices is put forward; and simulation experiments that measure road network resilience indices under both random and directed disturbances are established. Secondly, the results of the theoretical research are applied to the analysis of the structural resilience of intercity road networks in Zhejiang Province of China: (1) various factors, including existing intercity road network infrastructure, urban distribution, population growth, industrial structure change, and urban–rural integration in Zhejiang Province, are taken into account for comprehensive analysis, and the forecast of demands for the intercity road network in Zhejiang Province from 2021 to 2035 is proposed by qualitative and quantitative methodologies; (2) with the patterns of morphological growth and density distribution as spatial planning variables, four road network planning schemes are proposed for Zhejiang Province, and the corresponding road network topological models are then established; (3) the network resilience indices of the four planning schemes are measured by simulation experiments under random and directional disturbances; (4) the characteristics of the four planning schemes under random and directed disturbances are analyzed. The results are as follows: (1) the pattern of growth morphology is the primary factor influencing network resilience, where the growth shape of a grid is favorable for resisting both random and directed disturbances. Under random disturbance, the change in global network efficiency is primarily influenced by the pattern of density distribution, which then serves as the main influencing factor of the network collapse threshold under directed disturbance. (2) It is recommended that the intercity road planning scheme of “grid growth morphology plus collective increase of road density distribution” be adopted for Zhejiang Province from 2021 to 2035. (3) In light of the time-lag issue between population growth and the development of intercity road networks, this study proposes corresponding planning strategies for intercity road networks from two perspectives: spatial growth patterns and construction prioritization. The study findings provide references for the analysis of the correlation between road network structure and resilience.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658318</guid>
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    <item>
      <title>Topology optimization design and fractional order sliding mode control of hub motor-driven vehicle dynamic inertial suspension</title>
      <link>https://trid.trb.org/View/2691751</link>
      <description><![CDATA[To address issues such as the negative effects of vertical vibrations caused by the wheel hub motor and the unbalanced radial forces resulting from motor static eccentricity, a topological optimization design method is proposed for the Hub Motor-Driven Vehicle (HMDV) dynamic inertial suspension based on Fractional Order Sliding Mode Control (FOSMC). Firstly, a static eccentricity model for a four-phase 8/6-pole switched reluctance motor is established, analyzing the unbalanced radial forces generated by motor excitation under varying static eccentricity. Subsequently, the impact of the dynamic inertial suspension’s topological structure on suspension performance is studied under the influence of the wheel hub motor’s self-weight and motor static eccentricity. Several superior dynamic inertial suspension structures that enhance suspension performance are identified. Next, optimization algorithms are employed to optimize the parameters of the dynamic inertial suspension, determining the topological structure that optimizes suspension performance. Then, a quarter-HMDV dynamic inertial suspension model based on the Acceleration-Driven-Damping (ADD) control strategy is developed, followed by an analysis of the mechanism for suppressing negative vertical vibrations in HMDV. Finally, the dynamic inertial suspension based on ADD is taken as the reference model, and the dynamic inertial suspension based on FOSMC is built, and the simulation and single-channel experiment are carried out. The simulation and test data show that the controlled dynamic inertial suspension has obvious inhibition effect on the deterioration of vehicle suspension performance caused by wheel hub motor.]]></description>
      <pubDate>Mon, 20 Apr 2026 17:01:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691751</guid>
    </item>
    <item>
      <title>Network Robustness Improvement Based on Alternative Paths Consideration</title>
      <link>https://trid.trb.org/View/2579253</link>
      <description><![CDATA[Many transportation networks have complex infrastructures (road, rail, airspace, etc.). The quality of service in air transportation depends on weather conditions. Technical failures of the aircraft, bad weather conditions, strike of the company’s staff cause delays and disrupt traffic. How can the robustness of such networks be improved? Improving the robustness of air transportation would reduce the cascading delays between airports and improve the passenger journey. Many studies have been done to find critical links and nodes, but not so many analyze the paths. In this paper, we propose a new method to measure network robustness based on alternative paths. Besides improving the robustness of the French (respectively Turkish Airlines and European) low-cost flight network by 19% (respectively 16% and 6.6%), the method attempts to show the relevance of analyzing the network vulnerability from a path-based approach.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579253</guid>
    </item>
    <item>
      <title>Optimally Persistent Formation of AUVs With Model Uncertainty and Unknown Interaction Topology</title>
      <link>https://trid.trb.org/View/2610703</link>
      <description><![CDATA[Formation control of autonomous underwater vehicles (AUVs) has been regarded as the basis of many sophisticated marine missions. However, the complex marine environment and the weak acoustic communication on AUVs make it hard to achieve the formation task. This paper attempts to overcome the above challenge from graph theory and intelligent learning perspectives. A local topology estimator is first designed by observing the coupled state evolution of AUVs, such that the unknown interaction relationship of AUVs can be inferred on the basic of local sensing. Based on this, we adopt the graph direction and contraction to generate an optimally persistent topology for AUVs, whose aim is to reduce the communication redundancy and guarantee the topology connectivity. With the optimized network topology, a model-free inverse reinforcement learning (IRL) formation controller is developed for AUVs to keep the desired formation shape. The innovations can be summarized as follows: 1) the local topology estimator can reveal the interaction topology relationship of AUVs with multiple degrees of freedom (DOF); 2) the optimally persistent topology can balance energy efficiency and topology connectivity as compared to the neighboring rule-based solutions; 3) the IRL-based formation controller has better adaptability to the underwater unknown environment as compared to the traditional reinforcement learning solutions. Finally, simulation and experimental results are both conducted to verify the effectiveness of our solution.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:10:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610703</guid>
    </item>
    <item>
      <title>TLCO: Topological Link-Aware Task Co-Offloading Method for Joint V2V and V2I System</title>
      <link>https://trid.trb.org/View/2610650</link>
      <description><![CDATA[Joint Vehicle-to-vehicle (V2V) and Vehicle-to-Infrastructure (V2I) offloading presents an efficient approach to leverage surplus computing resources from neighboring devices, thereby expanding the coverage of computing resources supply in the context of the Internet of Vehicles. However, many studies overlook the significance of topological communications caused by the rapid movement of vehicles, privacy, and communication intentions. To achieve efficient task offloading when facing various topological link structures, we first propose a novel topological link-aware task co-offloading (TLCO) method designed for partially offloading in the joint V2V and V2I system. Next, we model the sequential subtasks offloading process as the Markov Decision Process (MDP) and utilize the Double Deep Q-Network (DDQN) algorithm to optimize the total delay of the proposed system. Additionally, we put forth a prediction framework named Sliding Time Windows and TLCO algorithm (STW-TLCO) to accurately forecast the computation load at various time windows using pulsed parameters. Extensive experimental results demonstrate the effectiveness and superiority of the proposed TLCO-DDQN algorithm in comparison to other Deep Reiforcement Learning (DRL)-based and Greedy-based approaches. Furthermore, the STW-TLCO algorithm exhibits high accuracy, with an R-squared value exceeding 96%, confirming its predictive capabilities.]]></description>
      <pubDate>Thu, 26 Mar 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610650</guid>
    </item>
    <item>
      <title>H²DGL: Adaptive Metapath-Based Dynamic Graph Learning for Supply Forecasting in Logistics System</title>
      <link>https://trid.trb.org/View/2617705</link>
      <description><![CDATA[The advanced logistics systems are increasingly transitioning towards integrated warehousing and distribution supply networks (IWDSN), where accurately forecasting supply capacity is essential for maintaining delivery capabilities that meet user demands. However, existing research often overlooks the impact of dynamic changes in network topology, resulting in limitations in capturing dynamic routing and diverse node responses. These limitations become particularly pronounced in the context of external events such as pandemics, heavy rain, and promotions. To address the above limitations, we propose  $\mathtt {H^{2}DGL}$ , a Hierarchical Heterogeneous Dynamic Graph Learning framework based on adaptive metapath aggregation, for forecasting supply capabilities in logistics systems. Specifically,  $\mathtt {H^{2}DGL}$  comprises three main modules: (1) Hierarchical Heterogeneous Node Representation, where the micro graph captures dynamic routing information through adaptive meta-path aggregation from routing and event view graphs, and the macro graph extracts spatial representations using bipartite graph learning. (2) The Dynamic Graph Encoding module integrates macro and micro features from different snapshots to derive unified node representations. (3) The Spatio-temporal Joint Forecasting combines spatial features with temporal features from a time-series encoder to predict future supply capacity. Extensive experiments on two real-world datasets from different cities demonstrate that  $\mathtt {H^{2}DGL}$  achieves state-of-the-art performance compared to advanced baseline models. The code is available at https://github.com/kaiwxai/H2DGL]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617705</guid>
    </item>
    <item>
      <title>Hysteresis behind a freeway bottleneck with location-dependent capacity</title>
      <link>https://trid.trb.org/View/2643248</link>
      <description><![CDATA[Macroscopic fundamental diagrams (MFDs) for traffic networks have gained theoretical and empirical support with new data collection technologies. However, well-defined MFD curves only exist for specific network topologies and are subject to disturbances, particularly hysteresis phenomena. This study examines hysteresis in MFDs and Network Exit Functions during rush hour conditions. We apply the LWR theory to a highway corridor with a downstream bottleneck and identify a figure-eight hysteresis pattern. We analyze the impact of road topology and demand patterns on hysteresis formation analytically. Empirical data from two bottlenecks provides statistical evidence that continuous bottlenecks cause less hysteresis than discontinuous ones. Our observations confirm counter-clockwise loops in real conditions, attributed to demand asymmetries through theoretical analysis. Numerical experiments using the Cell Transmission Model demonstrate that even slight capacity reductions in homogeneous sections can significantly decrease MFD hysteresis while maintaining downstream outflow, achievable through standard traffic control measures like ramp metering.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643248</guid>
    </item>
    <item>
      <title>Hierarchical control based on SPAT unidirectional heterogeneous communication topology for intelligent connected hybrid electric vehicle platoon</title>
      <link>https://trid.trb.org/View/2666888</link>
      <description><![CDATA[The rapid development of intelligent connected vehicle technology provides new solutions for eco-driving urban transportation. To address the problem of vehicle platoons stopping and waiting at consecutive signalized intersections and to investigate the following performance and fuel economy of platoons under unidirectional heterogeneous communication topologies, this study proposes a hierarchical control strategy for intelligent connected hybrid electric vehicle (HEV) platoons based on signal phase and timing (SPAT) and unidirectional heterogeneous communication topologies. The upper-level controller establishes a target speed model based on SPAT information and solves the optimal target speed using a model predictive control (MPC) algorithm, considering the platoon’s unidirectional heterogeneous communication topology and the variation in vehicle aerodynamic drag. The lower-level controller employs a deep deterministic policy gradient (DDPG) algorithm for energy management to achieve optimal power allocation, addressing the limitation of deep Q-learning (DQN) in handling continuous high-dimensional state spaces. Simulation results demonstrate that vehicle platoons with unidirectional heterogeneous communication topologies can satisfy various traffic constraints, achieving excellent following and passing performance. Compared with DQN, the DDPG-based energy management strategy improves fuel economy by 6.45%, 6.23%, 6.19%, and 6.25% under PF, PLF, TPF, and TPLF communication topologies, respectively.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666888</guid>
    </item>
    <item>
      <title>Analysis of the Topological Similarity of Street Networks in Cities Around the World Using Graph2vec</title>
      <link>https://trid.trb.org/View/2655506</link>
      <description><![CDATA[This study employs graph2vec, a graph-based machine learning method, to quantify the topological characteristics of street networks in 2,450 cities worldwide. The results reveal distinct regional differences, particularly among Asia, Europe, and America, and show that Asia exhibits notable internal diversity, with subregions displaying unique structural features. These patterns suggest that historical context and infrastructure development have shaped urban form. The findings underscore the limitations of directly applying insights from European and American cities to Asia and highlight the need for region-specific analyses. While this study focuses on vehicle-accessible networks, future work should incorporate multimodal transport systems, facility distributions, and topographic conditions to better capture urban morphology. Additionally, improving the interpretability of embedding techniques and comparing alternative graph learning methods are essential next steps for advancing applications in urban and transport planning.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655506</guid>
    </item>
    <item>
      <title>Spatiotemporal Generalization Graph Neural Network-Based Prediction Models by Considering Morphological Diversity in Traffic Networks</title>
      <link>https://trid.trb.org/View/2591157</link>
      <description><![CDATA[The morphological diversity, referring to the variations in traffic network topologies defined in this paper, often emerges and brings difficulties in successfully transferring a pre-trained prediction model from one traffic network to another. Moreover, most existing research primarily assumes that traffic data in source and target networks follow independent and identically distributed (i.i.d.) patterns, which is usually not consistent with real-world situations, particularly when considering morphological diversity. For this inconsistency, many efforts have been made, but they mainly concentrate on temporal aspects, which significantly differ from traffic prediction due to spatial and temporal correlations among road segments, influenced by variations in road topology and traffic behavior. This paper introduces a causality-based spatiotemporal out-of-distribution (OOD) generalization method, which is adaptable to most GNNs for diverse, large-scale, dynamic traffic systems with zero-shot. Furthermore, to enhance the generalization and adaptability of the proposed method, we introduce graph matching and equal-sized graph partitioning to alleviate spatial shift between the source and target traffic networks, reduce and align the scale of the networks. Experiments carried out on traffic flow datasets demonstrate that our method significantly improves the performance of various GNN-based traffic predictors in the situation of morphological diversity, achieving a maximum reduction in MAE of 33.08%. Compared to other OOD-driven baselines, our approach also shows a notable improvement, with up to a 40.58% decrease in MAE.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591157</guid>
    </item>
    <item>
      <title>Overall topological accessibility index (OTAI): A proposed indicator of urban transportation accessibility</title>
      <link>https://trid.trb.org/View/2642465</link>
      <description><![CDATA[The rapid and selective urbanization process leads to a series of socioeconomic and environmental problems related to urban mobility. The literature lacks a robust approach that integrates various urban mobility dimensions, as traditional methods for assessing the accessibility and connectivity of transportation networks often yield inconsistent results. In addition, the city of Viçosa (Minas Gerais, Brazil) requires a comprehensive evaluation of accessibility and connectivity in its primary and secondary road networks. To address these knowledge gaps, this study proposes and evaluates a new indicator called Overall Topological Accessibility Index (OTAI), designed to identify the most and least accessible regions of a city. This indicator combines the Connectivity Matrix, Associated Number, and Number of Indirect Links methods. To validate the proposed methodology, nine different network models were generated for Viçosa’s road system and subsequently transformed into topologically connected networks suitable for calculating connectivity and accessibility parameters. The results provided a holistic evaluation of urban accessibility and connectivity, in addition to a comprehensive analysis of the effects of various modifications to the road network, such as the implementation of ring roads. Non-central roads, network extremities, and one-way streets demonstrated low accessibility. The inclusion of a ring road resulted in a negligible improvement in the Connectivity Index of the road network but increased the OTAI of nearby roads. In conclusion, the OTAI proved to be a robust and accurate tool for a more integrated assessment of urban accessibility and connectivity, enabling targeted changes that promote transportation sustainability.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:01:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642465</guid>
    </item>
    <item>
      <title>Characterizing a Long-Time Evolution of Metro Network Topology: Evidence from Metro Networks in 34 Chinese Cities</title>
      <link>https://trid.trb.org/View/2663316</link>
      <description><![CDATA[The development of urban metro networks is crucial for planners and policymakers in addressing sustainable urban development. This study collected metro network data from 34 cities in China and utilized a three-dimensional model to perform a topological analysis of these networks, aiming to identify successful practices. The service degree of population demonstrated a strong correlation with the topology of urban metro networks. The topological analysis revealed three phases in the evolution of metro networks: (1) central densification; (2) outward expansion; and (3) preferential development. Throughout this process, the complexity of metro networks tends to reach saturation, and transfer convenience is enhanced, resulting in a reduction in the number of transfers required during travel. Furthermore, the implementation of ring lines was effective in improving the state features of metro networks during the early stages. Increasing the ratio of radial lines can maintain network simplicity and enhance transfer convenience. The findings of this study provide insights for policymaking in the development of urban metro systems.]]></description>
      <pubDate>Wed, 04 Feb 2026 08:56:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663316</guid>
    </item>
    <item>
      <title>Measuring robustness in uncertain topologies: a study of on-demand bus networks</title>
      <link>https://trid.trb.org/View/2614555</link>
      <description><![CDATA[Ensuring the robustness of bus networks is essential for delivering reliable and efficient mobility services to passengers. This paper addresses the challenge of assessing the robustness of on-demand bus networks, which are characterised by uncertain topology. We propose a framework based on a random multilayer bus network, from which we develop four topological metrics to quantify network robustness. Additionally, we conduct network attack simulations to derive simulation-based robustness indicators, which are acknowledged as golden rules in robustness measurements. Correlation analysis between the proposed metrics reveals a positive relationship between the topological properties and network robustness, validating the effectiveness of the topological metrics in assessing the robustness of networks with uncertain topology. This study fills a gap in the existing literature by providing a robustness analysis framework specifically tailored to on-demand bus networks, contributing to the design of more resilient on-demand bus systems.]]></description>
      <pubDate>Wed, 28 Jan 2026 14:41:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614555</guid>
    </item>
    <item>
      <title>Evolution of aggregate skeleton in asphalt mixture at meso-scale by DEM and topology</title>
      <link>https://trid.trb.org/View/2643760</link>
      <description><![CDATA[Asphalt mixture, the most common material used for road surfacing, is prone to various forms of distresses, such as rutting and fatigue. In asphalt mixtures, the aggregate skeleton acts as the primary load-bearing structure. However, its structural changes are challenging to quantify at the macro scale. In this study, aggregates were classified into three categories: disruption aggregate (DA), main skeleton aggregate (MSA) and central large aggregate (CLA). Then, algebraic connectivity (Lₐ), effective average coordination number (Kₙ) and clustering coefficient (Cᵢ) were used to quantitatively characterise their structural properties. Furthermore, an uniaxial compression test was conducted using DEM to analyse structural evolution of the aggregate skeleton at three stages: initial loading, peak stress and failure. It was found that during loading, DA contacts were lost, weakening inter-particle connections; MSA saw a decrease in Kₙ and an increase in unstable particle proportion, reducing bearing capacity; and CLA experienced a decline in local aggregation, damaging structural uniformity, all leading to progressive failure. This study analysed the evolution of the aggregate skeleton structure under uniaxial compressive loading and evaluated the progressive failure of the inter-particle contact structure throughout the loading process.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643760</guid>
    </item>
    <item>
      <title>Topological characteristic evolution of the force chain network in the compaction process of the aggregate blend</title>
      <link>https://trid.trb.org/View/2643727</link>
      <description><![CDATA[An aggregate blend plays an important role in the performance of the asphalt mixture. The characteristic study on the aggregate blend is not comprehensive enough. This study analysed the evolution behaviour of the topological characteristic of the force chain network (FCN) during the compaction of the aggregate blend based on a complex network theory. A discrete element method (DEM) was selected to conduct the compaction tests of aggregate blends with three typical gradations. According to the contacts between different function particles, the whole FCN was divided into the FCN formed by the contacts only between main skeleton aggregates (S–S) formed by the contacts between the main skeleton aggregates and disruption aggregates (S–D) and those formed by the contacts only between disruption aggregates (D–D). A complex network theory was introduced, and the construction method of FCN topology graph was proposed. The evolution of the topological characteristics of FCN was analysed. The results showed that the suspended-dense structure has better compactability than that of the skeleton structure. Big particles are more stable than small particles during compaction. The interlocking effect of the aggregate blend gradually becomes better with the compaction process until its structure becomes stable. The compaction has a little influence on the contribution of S–S, S–D and D–D to the number of load transfer paths and the amount of load transfer.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643727</guid>
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