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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>Interactive Data–Knowledge Synergy via Guidance and Feedback for Railway Risk Decision-Making</title>
      <link>https://trid.trb.org/View/2682018</link>
      <description><![CDATA[Railway risk decision-making is increasingly challenging due to the volatility, uncertainty, complexity, and ambiguity (VUCA) inherent in operational environments. Traditional approaches rely separately on data-driven methods, which excel in prediction but lack knowledge-driven methods, which are struggle to adapt to dynamic conditions, cannot fully address modern railway risks. To overcome these limitations, this paper proposes an innovative data-knowledge synergetic framework for railway risk decision-making. The framework incorporates a “guidance and feedback” mechanism to facilitate the interaction between data-driven and knowledge-driven models for complementary enhancements. A context-based railway risk knowledge base is constructed for the knowledge-driven model and an algorithm deconstruction method is designed for the data-driven model to enable the information exchange and results fusion between the two. A real-world railway case study validates the framework, demonstrating improved decision-making efficiency, adaptability, and interpretability in complex railway risk context.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682018</guid>
    </item>
    <item>
      <title>From Connectivity to Complexity: The Influence of High-Speed Rail on Urban Knowledge Complexity</title>
      <link>https://trid.trb.org/View/2659837</link>
      <description><![CDATA[Knowledge complexity is a key determinant of regional competitiveness, yet the mechanisms and micro-level carriers through which transportation infrastructure shapes it remains insufficiently understood. This study examines the impact of high-speed rail (HSR) on knowledge complexity using patent and socio-economic data from 268 Chinese cities over 2005–2020, applying a multi-period difference-in-differences approach. Results show that HSR significantly enhances urban knowledge complexity, and the findings remain robust after addressing endogeneity concerns. Mechanism analysis reveals that HSR promotes complexity primarily through diversified agglomeration and network externalities, while specialized agglomeration has no significant effect. Moreover, HSR reshapes the relationship between agglomeration and network effects by substituting localized specialization with networked knowledge flows and enhancing the innovative potential of diversification through cross-regional complementarities. At the micro level, HSR triggers knowledge combination through two channels: a sharing mechanism that emphasizes collaborative interactions and collective knowledge externalities, and a matching mechanism that facilitates the strategic acquisition and recombination of external knowledge via technology transfers. By integrating agglomeration and network externality frameworks, this study provides empirical evidence on how HSR shapes urban knowledge complexity. The findings offer China-specific policy implications and transferable insights for regions pursuing innovation-driven growth through improved connectivity.]]></description>
      <pubDate>Thu, 28 May 2026 09:06:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659837</guid>
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    <item>
      <title>Road Network Robustness of Financial Centre Cities: Rich but not Effective</title>
      <link>https://trid.trb.org/View/2670067</link>
      <description><![CDATA[Complex networks provide a powerful framework to understand complex systems in nature and society, offering insights into the basic principles governing their organization and dynamics. As a fundamental problem of complex network, the network robustness analysis emerges as critical to theoretical and empirical research. Rather than focusing solely on the robustness of road networks, here we further investigate the robustness-effectiveness, which is defined as the comparison of the network robustness to an Erdös-Rényi random network with equivalent network size and edge density. Through extensive empirical investigations from economically top-ranking cities guided by the Global Financial Centres Index, we surprisingly discovered that North American and South American cities exhibit superior robustness, with African cities outperforming those in Europe, Asia, and Australia in select instances. A critical discovery is that intersections critical to network integrity are not identified by high centrality but rather by their peripheral positioning. Furthermore, through correlations between cities robustness ranking and corresponding GFCI rankings, it is worth noting that cities which experienced the most dramatic declines in ranking often have English as an official language, whereas cities that display the most significant rises in rank typically feature Portuguese and Spanish. These results offer new insights into the dichotomy between economic prominence and infrastructural robustness, and provide a novel viewpoint for network design and optimization with significant potential applications in man-made city systems.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670067</guid>
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    <item>
      <title>Modelling, Simulation and Optimisation of Complex Systems in Maritime Transport</title>
      <link>https://trid.trb.org/View/2703858</link>
      <description><![CDATA[Maritime transport systems represent complex socio-technical environments characterised by strong interdependencies between technical, organisational, environmental, and human subsystems, as well as by pronounced uncertainty and dynamic behaviour. Traditional deterministic planning approaches are often insufficient to adequately capture such complexity or to support robust operational decision-making. This paper examines the modelling and optimisation of complex maritime transport systems by integrating network-based optimisation and stochastic modelling approaches. The methodological framework combines operational research techniques with simulation in order to represent maritime transportation as a directed time–space network with stochastic demand and time windows. Uncertainty related to operational disturbances, port congestion, and variable demand is explicitly incorporated into the optimisation process. A case-oriented application demonstrates how stochastic network-based optimisation can improve routing, scheduling, and recovery strategies compared to purely deterministic approaches. The results indicate that controlled flexibility in arrival times and routing decisions leads to improved operational performance, enhanced resilience, and better trade-offs between fuel consumption, service reliability, and recovery costs. The proposed framework serves as a decision-support tool for maritime operations, providing structured analytical support, while preserving the role of human judgment in safety-critical environments. By bridging systems theory, operational research, and applied maritime modelling, the study contributes to the development of robust decision-support approaches for complex and uncertain maritime transport systems.]]></description>
      <pubDate>Wed, 20 May 2026 13:50:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703858</guid>
    </item>
    <item>
      <title>Hierarchical analysis of spreading dynamics in complex systems</title>
      <link>https://trid.trb.org/View/2646695</link>
      <description><![CDATA[Modeling spreading dynamics on spatial networks is crucial to addressing challenges related to traffic congestion, epidemic outbreaks, efficient information dissemination, and technology adoption. Existing approaches include domain-specific agent-based simulations, which offer detailed dynamics but often involve extensive parameterization, and simplified differential equation models, which provide analytical tractability but may abstract away spatial heterogeneity in propagation patterns. As a step toward addressing this trade-off, this work presents a hierarchical multiscale framework that approximates spreading dynamics across different spatial scales under certain simplifying assumptions. Applied to the Susceptible-Infected-Recovered (SIR) model, the approach ensures consistency in dynamics across scales through multiscale regularization, linking parameters at finer scales to those obtained at coarser scales. This approach constrains the parameter search space, and enables faster convergence of the model fitting process compared to the non-regularized model. Using hierarchical modeling, the spatial dependencies critical for understanding system-level behavior are captured while mitigating the computational challenges posed by parameter proliferation at finer scales. Considering traffic congestion and COVID-19 spread as case studies, the calibrated fine-scale model is employed to analyze the effects of perturbations and to identify critical regions and connections that disproportionately influence system dynamics. This facilitates targeted intervention strategies and provides a tool for studying and managing spreading processes in spatially distributed sociotechnical systems.]]></description>
      <pubDate>Mon, 18 May 2026 16:36:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646695</guid>
    </item>
    <item>
      <title>System-of-systems Safety for Low-altitude Aviation Transportation</title>
      <link>https://trid.trb.org/View/2660570</link>
      <description><![CDATA[Low-altitude (LA) transportation is rapidly evolving as a new sector of traditional aviation industry. Specifically, existing aviation safety studies were largely developed under traditional aviation System-of-systems (SoS) architectures, and primarily address system-level risks (human, vehicle, communication-navigation-surveillance, or operational management) with clear system boundaries. As a new SoS, LA SoS may have new emergent risk types and cross-domain cascading risk propagation via new architecture. This paper considers the LA transportation SoS from architecture viewpoint, and analyzes its new characteristics and risk patterns. Therefore, we propose LA SoS safety engineering framework to handle these possible new challenges through architecture design, testing & evaluation, and safety management. Accordingly, our SoS safety engineering framework mainly includes: architecture design (human-automation safety control and digital flight rules), testing & evaluation (traceable design-to-verification and equipment safety requirement), and safety management (monitor-assess-mitigate for in-time risk prediction and mitigation). Our safety engineering framework aims to provide possible solution for this new complex system of LA aviation transportation.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2660570</guid>
    </item>
    <item>
      <title>Reliability and Statistics in Transportation and Communication: Human Sustainability and Resilience in the Digital Age: Selected Papers from the 24th International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication, RelStat-2024, Riga, Latvia, September 25-28, 2024</title>
      <link>https://trid.trb.org/View/2579135</link>
      <description><![CDATA[This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice. It presents the most relevant findings discussed at the 24th International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication (RelStat 2024), which took place as a hybrid event on September 25-28, 2024, in/from Riga, Latvia. The chapters span a broad spectrum of advanced theories and methods, with a special emphasis on smart technologies and algorithms for enhancing sustainability and resilience of transport systems in various sectors.]]></description>
      <pubDate>Thu, 16 Apr 2026 16:54:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579135</guid>
    </item>
    <item>
      <title>A deep bidirectional spatio-temporal information-enhanced network for predicting complex vessel behavior trajectories</title>
      <link>https://trid.trb.org/View/2656499</link>
      <description><![CDATA[Vessel trajectory prediction (VTP) is of great significance for ensuring maritime traffic safety and realizing intelligent shipping management. However, the nonlinear dynamics and long-term dependencies of complex vessel behaviors in real-world maritime settings present major challenges to trajectory prediction. Graph based spatio-temporal network modeling has become a promising solution due to its ability to effectively explore spatial topology and temporal dependencies. We propose a deep bidirectional spatio-temporal information enhanced complex vessel behavior trajectory prediction framework, named STBiNet-PMDA. The framework predicts data from an automatic identification system (AIS) through a bidirectional information position encoding-decoding module. In the position encoding stage, a bidirectional spatio-temporal graph convolutional network (ST-GCN) is constructed and the feature mapping space is expanded through polymorphic mapping bidirectional gated recurrent unit (PM-BiNet) to enhance the model’s ability to express nonlinear variations and complex motion patterns. In the position decoding stage, the deformable attention (DA) mechanism is introduced and fused with the feature extraction module to improve the model’s sensitivity and prediction accuracy to complex changes in vessel behavior. The experimental results on two real-world datasets show that STBiNet-PMDA achieves average improvements of 59.73%, 60.87%, 59.45% and 59% in MAE, RMSE, FDE and AED compared to the optimal baseline. Especially in modeling complex vessel behavior, this prediction framework exhibits excellent performance and provides a new solution for intelligent shipping and trajectory prediction.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:40:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656499</guid>
    </item>
    <item>
      <title>Modeling, Evaluation, and Mitigation of Maritime Traffic Complexity in Complex Waters</title>
      <link>https://trid.trb.org/View/2610652</link>
      <description><![CDATA[Accurately interpreting regional traffic situations plays a pivotal role in emerging intelligent transportation systems, particularly in the evaluation of traffic states to realize the implementation of rational interventions. Nonetheless, existing studies face challenges when it comes to unveiling the complex nested interactions among multiple ships while simultaneously factoring in various influential elements for precise collision risk evaluation. This paper aims to develop a comprehensive methodology for collectively modeling, evaluating, and mitigating maritime traffic complexity, to enhance the comprehension of traffic patterns and guide anti-collision management in complex waters. First, a novel ship domain-based approach is proposed, incorporating individual ship attributes, relative bearing, ship motion dynamics, and restricted water geography to realize the accurate evaluation of ship-pair conflict risk. Subsequently, advanced motif structure-based indicators and a network disintegration model are merged to provide a thorough and nuanced characterization of the topological dependencies among multiple conflicts within a specified maritime region. Simultaneously, a comprehensive complexity evaluation approach, combining Principal Component Analysis (PCA) and a Fuzzy Clustering Iterative (FCI) method, is employed to achieve dependable parameterization and classification of traffic complexity levels. Finally, the collective impact of multiple interdependent conflicts on overall traffic complexity mitigation is investigated to support the identification of key influential conflicts that should take precedence in joint resolution efforts. Extensive experimental analyses based on Automatic Identification System (AIS) data are carried out to validate the effectiveness of the proposed methodology. These analyses demonstrate its applicability in accurately assessing conflict risk, hierarchically categorizing traffic complexity levels, and providing guidance for joint conflict resolution endeavors. Consequently, this methodology holds significant promise for bolstering the growth of intelligent transportation service systems and facilitating the automation of maritime traffic management.]]></description>
      <pubDate>Thu, 26 Mar 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610652</guid>
    </item>
    <item>
      <title>Safety analysis of human-machine interaction of autonomous ships using system theory and complex networks</title>
      <link>https://trid.trb.org/View/2676848</link>
      <description><![CDATA[Maritime Autonomous Surface Ships (MASS) are expected to be deployed widely due to their potential to enhance operational efficiency and reduce costs. Ensuring their success requires a new high level of safety analysis in Human-Machine Interaction (HMI). However, the task is particularly challenging under complex and dynamic maritime conditions. To address this challenge, this study proposes an advanced safety analysis framework that integrates Systems Theory Process Analysis (STPA) with Complex Network (CN) theory, namely STPA-CN, for analysing MASS HMI risks. To improve the precision of network analysis, a two-stage adaptive node ranking algorithm called MI-WLR is developed, which incorporates Mutual Information (MI) theory into the Weight LeaderRank (WLR) structure. The framework contains four components: (1) constructing the MASS HMI risk evolution Network (MHN) based on CN modelling and STPA outcomes; (2) analysing the topological characteristics of the MHN; (3) applying MI-WLR to rank the importance of risk nodes; and (4) conducting robustness analyses to validate the model's effectiveness. The results reveal that system-level hazards and accidents serve as key hubs in the MHN, with human factors, interface design, and environmental conditions exerting diverse degrees of influence. Notably, critical risks are often embedded within the human–machine interface, and targeting high-ranking nodes can effectively disrupt risk propagation pathways. This study fills an important research gap by introducing a novel and scalable framework for MASS HMI safety assessment, providing both theoretical and practical support for risk mitigation and the secure integration of autonomous technologies in maritime operations.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676848</guid>
    </item>
    <item>
      <title>Two-Stage Framework Combining Derivative-Free MCMC Sampling and Transport Maps for Black-Box Bayesian Inverse Problems</title>
      <link>https://trid.trb.org/View/2616793</link>
      <description><![CDATA[This study addresses the challenge of estimating Bayesian posterior distributions, a key task in uncertainty quantification, particularly within settings where the mapping from parameters to observations is a black box. Such models, often representing complex, high-dimensional stochastic systems, can be based on physical equations, surrogate models, or data-based machine learning functions, allowing only forward evaluations. Considering real-world noisy observations, we propose a two-stage framework. First, derivative-free Markov chain Monte Carlo (MCMC) methods sample the posterior. Second, these samples optimize a transport map (using polynomial or neural network bases) to construct a closed-form approximation of the posterior. This functional representation aids in characterizing parameter uncertainty and enables efficient uncertainty propagation. We compare the basis function choices and demonstrate the approach on Bayesian inverse problems for the heat equation and generalized Lotka–Volterra equations.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616793</guid>
    </item>
    <item>
      <title>Anthropomorphic Trajectory Forecasting for Multiagent Systems in Complex Environments</title>
      <link>https://trid.trb.org/View/2607937</link>
      <description><![CDATA[With the widespread application of graph-free autonomous driving, the importance of trajectory prediction has become increasingly prominent. End-to-end prediction models, while solving the prediction problem, have high computational requirements and cannot be applied on vehicles with low computational platforms. Based on this, this paper proposes an anthropomorphic multiagent trajectory prediction model (ATFMA), which only requires limited information, including trajectories, to make trajectory predictions such as humans in complex road scenarios, freeing itself from the constraints of high-precision maps and with low computational requirements. The algorithm takes the positional information of multiple agents as input, smooths trajectory noise by introducing an improved Gaussian filtering algorithm, and simultaneously performs trajectory vectorization. Then, based on the human perspective, it extracts features such as speed, acceleration, distance, and steering angle from the trajectory. Finally, the model is trained using graph neural networks and attention mechanisms. The model is validated and analyzed using the Argoverse data set. Based on the anthropomorphic encoding method, the model not only improves the interpretability of the algorithm but also accurately identifies intentions. Moreover, regarding prediction accuracy, the minimum average distance and final average distance prediction accuracy improved by 4.83% and 6.48%, respectively, compared to the Graph Attention Network Transformer (GAT-Transformer) algorithm. The proposed ATFMA method in this paper uses the trajectory features of agents and their neighboring agents as input without the need for perception systems such as cameras and radars. In addition, the method has low computational requirements for vehicles in practical applications, showing good universality and practicality.]]></description>
      <pubDate>Mon, 15 Dec 2025 10:34:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2607937</guid>
    </item>
    <item>
      <title>Evaluating High-Speed Rail Operation Safety through an Integrated Framework: A Case Study in China</title>
      <link>https://trid.trb.org/View/2630565</link>
      <description><![CDATA[Currently, high-speed railroad operation safety, one of the safest modes of transportation, is highly emphasized. Despite the high safety levels of high-speed rail in China, the increasing complexity of geographical and social environments has introduced new challenges to the evaluation of high-speed railroad operation safety. Existing evaluation methods, which have some limitations, may not fully address these challenges. Therefore, this study establishes a six-dimensional comprehensive evaluation indicator system, including personal, equipment, quality, legal, environmental, and monitoring, and proposes a three-stage comprehensive evaluation framework. This new framework combines the best–worst method, Decision-Making Test and Evaluation Laboratory, and interpretive structural modeling. In addition, the framework was validated with the Guangzhou-Shenzhen-Hong Kong High-Speed Railway as a typical case of dual complexity. The study findings suggest that equipment and monitoring factors, classified as effect and cause groups, exert the most significant influence on operational safety. Optimal management strategies enhance operational safety by prioritizing risk monitoring, disseminating risk knowledge, conducting regular equipment inspections, and ensuring train condition. This study helps to identify the key factors for enhancing operation safety, optimizing safety management efficiency, and providing broad insights for high-speed railroad operation managers and decision-makers.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:25:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630565</guid>
    </item>
    <item>
      <title>Driver Behavior Recognition in Complex Driving Scenarios via Deep Attention Network with Geometric–Spatial Fusion Features</title>
      <link>https://trid.trb.org/View/2625893</link>
      <description><![CDATA[Distracted driving is the primary cause of traffic crashes, and research on driver behavior recognition (DBR) has the potential to reduce the number of crashes caused by those distractions. However, existing DBR networks are trained and validated on a single data set, which leads to excessive overfitting to specific scenarios and limits their generalization to other scenarios. In addition, these networks are limited to considering contextual geometric features for recognition, failing to emphasize the importance of the driver’s skeletal spatial features, which restricts their ability to capture the crucial visual distinctions among various driver behaviors. Therefore, to address the previous issues, a deep attention network with geometric–spatial fusion features (DAN–GSFF) is proposed. Specifically, the DAN–GSFF integrates the image’s global and driver’s pose information as dual input, simultaneously considering the contextual geometric and skeletal spatial features for recognition. By embedding a multidimensional collaborative visual attention module in DBR (MCA–DBR), DAN–GSFF is guided to selectively focus on the driver’s local detailed features. Furthermore, a large-scale and diverse data set (SAA13) is integrated, which encompasses a wide variety of scenarios and includes data from 272 drivers, providing a more realistic representation of real-world scenarios. Experimental results demonstrate that DAN–GSFF outperforms other state-of-the-art models, achieving 90.11% accuracy and 96.2 frames per second on the SAA13 comprehensive data set. These results, with additional real time verifications on video streams, indicate that the DAN–GSFF exhibits strong recognition performance and robust generalization ability in complex driving scenarios.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:24:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625893</guid>
    </item>
    <item>
      <title>Reliability and Statistics in Transportation and Communication: Selected Papers from the 23rd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication: Digital Twins - From Development to Application, RelStat-2023, October 19-21, 2023, Riga, Latvia</title>
      <link>https://trid.trb.org/View/2579140</link>
      <description><![CDATA[This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice. The book presents the most relevant findings discussed at the 23rd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication (RelStat 2023), which took place as a hybrid event on October 19 – 21, 2023,  in/from Riga, Latvia. It spans a broad spectrum of advanced theories and methods, giving a special emphasis to the digitalization of transport systems, as well as smart, artificial intelligence, and digital twins applications.]]></description>
      <pubDate>Thu, 20 Nov 2025 17:07:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579140</guid>
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