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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Toward Safer Highways: Data-Driven Approach to Detecting Aggressive Driving Using Connected Vehicle Technologies</title>
      <link>https://trid.trb.org/View/2691792</link>
      <description><![CDATA[Aggressive driving behaviors such as tailgating and cutting off pose serious highway safety risks, especially for trucks. Timely detection of these behaviors can enable real-time interventions (e.g., automated driver warnings or vehicle safety system activation) to prevent crashes. This study presents a machine learning approach to detect tailgating and cut-off events using data from a high-fidelity driving simulator. Forty participants drove a truck in mild and heavy traffic scenarios within a connected vehicle (CV) environment, providing rich data for analysis. We fused four data sources—vehicle kinematics, CV-based metrics, road characteristics, and driver demographics—into five feature combinations to evaluate their predictive power. Four classification models (Artificial Neural Network, Support Vector Machine, Random Forest, and XGBoost) were trained on these feature sets. Performance evaluation across traffic scenarios shows that models leveraging CV data significantly outperform those using only traditional data, achieving high accuracy in identifying aggressive behaviors. Integrating CV features with conventional kinematic data substantially improved tailgating and cutting-off detection, underscoring the promise of CV technology for enhancing highway safety.]]></description>
      <pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691792</guid>
    </item>
    <item>
      <title>Optimization of Train Combination Strategy in Heavy-Haul Railway Technical Station</title>
      <link>https://trid.trb.org/View/2113851</link>
      <description><![CDATA[By analyzing the train combination operation flow and characteristics in the technical station at the beginning of heavy-haul railway, and considering the constraints of route conflict between departure route and locomotive running track, route conflict within arrival-departure tracks, the number of arrival-departure tracks, a mixed integer programming model was constructed with the aim of maximizing the train carrying capacity from the technical station in unit time. Finally, analyze the model through a practical example, the results indicated that within four hours, compared with the actual field experience combination scheme compilation method, the optimized train combination strategy can improve the transportation capacity in the technical station by 13.33%., thus verifying the feasibility and applicability of the model and algorithm.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113851</guid>
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    <item>
      <title>Evaluation and spatiotemporal analysis of low-carbon efficiency of the “ship-port” system</title>
      <link>https://trid.trb.org/View/2688649</link>
      <description><![CDATA[Evaluating the port low-carbon efficiency is essential for promoting decarbonization and mitigating climate change. This paper firstly constructs a two-stage ‘ship-port’ system, and then based on data from sources such as ship automatic identification system (AIS) data, the ‘bottom-up’ and ‘top-down’ methods are employed to measure and analyze carbon dioxide (CO₂) emissions at both the ship and port stages. Subsequently, a multi-period network data envelopment analysis (DEA) model is introduced, which addresses the limitation of traditional models that evaluate efficiency independently across different periods, thereby facilitating comparability of efficiency over time. The proposed model is applied to evaluate the dynamic low-carbon efficiency of both the overall and internal stages of the ‘ship-port’ system, and to identify the key links contributing to system inefficiency. Then the paper analyzes the temporal trends and spatial distribution characteristics of efficiency. The research conclusion finds that the system efficiency shows a slow and fluctuating upward trend over time, and the differences within regions constitute the primary cause of overall differences of port low-carbon efficiency. Finally, the targeted improvement strategies are proposed. The findings offer valuable insights for emission reduction policy of the government and enterprises, and contribute to the theoretical framework of network DEA.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688649</guid>
    </item>
    <item>
      <title>Understanding the determinants of fleet size optimization in dry bulk shipping: insights from a systematic literature review and Delphi study</title>
      <link>https://trid.trb.org/View/2688645</link>
      <description><![CDATA[Fleet optimization is a highly critical decision-making process for dry bulk shipping companies. Shipowners must navigate a range of factors, such as freight rates, shipping market cycles, regulatory changes, and economic conditions to maintain fleet profitability and operational efficiency. Despite having access to abundant market data, many shipowners and brokers in the dry bulk shipping industry periodically make mistakes in balancing the optimum fleet size. This research addresses the challenge and explores the complex decision-making process involved in fleet size optimization by highlighting the key indicators that should ideally be considered to enhance the process. To assist companies and brokers in the dry bulk shipping industry, the study offers a systematic and holistic approach to the challenge of fleet optimization. A novel conceptual framework was developed through the integration of systematic literature review and Delphi study, identifying seven primary dimensions and fifty essential determinants for determining optimal fleet size. The obtained dimensions include ‘Company Specifications,’ ‘Financial Variables,’ ‘Shipping Markets’ Variables,’ ‘Ship Specifications and Costs,’ ‘Shipowners’ Characteristics and Preferences,’ ‘Political and Legal Factors,’ and ‘Ports and Shipping Routes.’ The findings aim to minimize inefficiencies in fleet management and support more consistent strategic decision-making in the dry bulk shipping industry.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688645</guid>
    </item>
    <item>
      <title>Dynamic port resilience assessment in the maritime network: modeling flow evolution and cascading failure</title>
      <link>https://trid.trb.org/View/2656318</link>
      <description><![CDATA[Ports are critical to international trade but are highly vulnerable to disruptions, which can trigger cascading congestion across maritime networks. Evaluating port resilience under such dynamic conditions is challenging. This paper proposes a framework to assess dynamic port resilience after network failures, integrating dynamic allocation models with both resilience triangle and moment‑based metrics. Key features include: (1) Queuing theory to capture weekly variations in port operations, estimating efficiency dynamically; (2) Prediction and inertia parameters for shipping routes and ports to reflect adaptive adjustments in the network; (3) Differentiated reallocation strategies to simulate maritime-specific characteristics. A case study on the Asia-Europe maritime network validates the framework, revealing spatial and temporal heterogeneity in congestion patterns. Results show that higher prediction parameters, especially for shipping routes, stabilize resilience faster, while moderate inertia parameters enhance resilience, with optimal effects achieved through balanced route inertia and port inertia. Moment‑based metrics further reveal contrasting pathways. High prediction produces faster but volatile recovery, while moderate inertia supports more stable and coordinated adaptation. Validation against the 2021 Yantian Port disruption confirms that adaptive settings with strong prediction and moderate inertia yield operational patterns similar to the observed data. These findings reveal key trade‑offs between agility and stability and provide operational insight into mitigating congestion and enhancing resilience in maritime networks.]]></description>
      <pubDate>Thu, 09 Apr 2026 10:08:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656318</guid>
    </item>
    <item>
      <title>Machine learning in road freight transportation: A decision-making perspective</title>
      <link>https://trid.trb.org/View/2684426</link>
      <description><![CDATA[Road freight transportation represents the dominant mode for inland freight transportation and is a critical component of logistics systems. Decisions in this domain are inherently complex due to the scale of operations, the numerous actors involved, and the dynamic operating environment. Machine learning (ML) represents a valid tool to address this complexity. However, literature on the topic is largely technical and focused on specific applications, lacking broader managerial insights that researchers and practitioners can leverage when applying ML to road freight transportation management (RFTM) decisions. To address this gap, this paper presents a systematic literature review investigating the characteristics of decision making when ML is applied to RFTM, encompassing both the managerial and technical aspects. The findings indicate that, while numerous studies focus on vehicle routing problems, other decisions within RFTM remain largely underexplored. Furthermore, only a limited number of studies develop and validate machine learning (ML) solutions using real-world data, thereby constraining the assessment of their practical applicability and impact. In addition, drawing from the analysis of the review results, this study proposes a conceptual framing of ML applications in RFTM, depicting ML as a potential mitigator of the limitations associated with bounded rationality in RFTM decision-making. This paper lays the foundation for future research on the application of ML for RFTM. It also proposes a replicable approach for analyzing data-driven decisions in other contexts. Moreover, it offers practical guidance to decision makers by highlighting the elements that ML can act upon, thus supporting its adoption in practice.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:41:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684426</guid>
    </item>
    <item>
      <title>A nonlinear methodology for the sloshing assessment in the membrane-type LNG cargo containment system</title>
      <link>https://trid.trb.org/View/2653035</link>
      <description><![CDATA[With tightening environmental regulations, demand for eco-friendly fuels has been increasing, driving a steady rise in orders for LNG-fueled ships and LNG carriers. In particular, the demand for membrane-type LNG carriers, which offer excellent safety and economic efficiency, has been growing significantly. However, sloshing during operation significantly impacts the structural integrity of cargo tanks, and high-amplitude sloshing loads can cause permanent damage. Traditional linear-based sloshing assessment methods provide conservative, relatively safe results but fail to capture the nonlinear characteristics of actual sloshing loads adequately. Although several classification societies have proposed assessment procedures that incorporate structural nonlinearity, further refinement is required for practical application. In this study, an improved sloshing assessment methodology that considers structural nonlinearity was proposed, based on Lloyd's Register (LR) procedures, and applied to the MARK III Flex cargo containment system (CCS) of a 174K LNG carrier. Model tests were conducted to define the expected ranges of peak pressure and rising time. In addition, numerical simulations incorporating strain-rate effects and the plastic deformation behavior of R-PUF (reinforced polyurethane foam) under sloshing loads were performed, and the predicted dynamic responses were validated against experimental results. Furthermore, empirical formulations of the dynamic response factor were derived for different failure modes of CCS components. Finally, the utilization factor of the MARK III Flex CCS was evaluated using sloshing loads obtained from the 174K LNG carrier model tests, thereby validating the practicality and reliability of the proposed methodology. The proposed method quantitatively accounts for the structural nonlinear response of the MARK III Flex CCS to sloshing loads while considering hull stiffness, thereby balancing prediction accuracy and engineering practicality for practical applications.]]></description>
      <pubDate>Mon, 06 Apr 2026 08:50:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2653035</guid>
    </item>
    <item>
      <title>Investigation of wet-towing of elevated railway floating bridge in inland rivers considering shallow water wave: A case study of the Yangtze River</title>
      <link>https://trid.trb.org/View/2653003</link>
      <description><![CDATA[This study investigates the wet-towing dynamics of an Elevated Railway Floating Bridge (ERFB) designed for emergency repair operations in inland waterways. A coupled numerical model was developed using SESAM and MATLAB co-simulation to analyze the ERFB responses under combined wave and current loading, with validation performed against established benchmarks. Based on measured hydrological conditions from the Yangtze River, the research examines shallow-water wave attenuation effects and the influence of current magnitude and direction on towing safety. Results demonstrate that shallow-water effects significantly alter motion predictions. At 9 m water depth, the TMA spectrum yields maximum heave and pitch motions 25 % and 31 % lower than the JONSWAP spectrum, respectively, confirming substantial wave energy attenuation in depth-limited conditions. Despite increased wave energy dissipation at longer periods, structural resonance remains the dominant factor governing motion response. Current analysis reveals that velocity and direction collectively influence system dynamics and cable tension through modifications to encounter frequency and damping characteristics. Critically, down-current conditions at low to medium velocities are identified as producing the largest response, representing the most hazardous operational scenario. The findings provide quantitative design criteria for safe towing operations of inland floating structures in shallow-water environments.]]></description>
      <pubDate>Mon, 06 Apr 2026 08:50:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2653003</guid>
    </item>
    <item>
      <title>Multi-Resolution Deep Learning for Coupler Force Prediction in 20,000-Ton Heavy-Haul Trains</title>
      <link>https://trid.trb.org/View/2686159</link>
      <description><![CDATA[Heavy-haul trains play a crucial role in long-distance bulk transportation, yet their enormous mass and kilometer-scale length lead to complex longitudinal interactions and high coupler forces, which threaten operational safety. Conventional mechanism-based models, while accurate, are computationally expensive and unsuitable for real-time prediction. To address this limitation, this study develops a data-driven prediction framework that combines physics-based modelling and deep learning. A detailed longitudinal dynamics model of a 20,000-ton train operating on the Shuohuang Railway is constructed, incorporating traction, electrical braking, and resistance characteristics to compute coupler forces under varying gradients and curvature conditions. Based on this model, a QP-based optimization algorithm and a high-fidelity simulation platform are used to generate multi-strategy operating datasets that balance energy efficiency, punctuality, and ride comfort. The resulting data are processed using normalization and sliding-window segmentation to form supervised learning samples. A multi-resolution dual-stream LSTM (MRDS-LSTM) and its attention-enhanced variant (MRDS-LSTM–Attn) are then proposed to capture both short-term fluctuations and long-term temporal trends. Compared with RNN, GRU, LSTM, Bi-LSTM, NLSTM, CNN-LSTM, CNN-NLSTM, CapNet-NLSTM, Transformer, and Informer baselines, the proposed model achieves the highest prediction accuracy with MRDS-LSTM-Attn achieves an MAPE of 2.57%, and $R^2$ of 0.9888. The results demonstrate that the proposed framework effectively bridges physical modelling and data-driven prediction, achieving up to 706$\times$ faster inference than traditional solvers. It provides a practical foundation for intelligent heavy-haul train operation, supporting real-time coupler force monitoring, predictive safety control, and future extensions to pneumatic braking and field data validation.]]></description>
      <pubDate>Fri, 03 Apr 2026 12:13:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686159</guid>
    </item>
    <item>
      <title>A latent risk factor analysis guiding policy interventions for inland maritime safety</title>
      <link>https://trid.trb.org/View/2667043</link>
      <description><![CDATA[Inland waterway safety is a pressing policy priority in China's transport sector, where effective regulation and targeted interventions are vital to managing accident risks in the world's busiest inland shipping network. While policymakers have increasingly focused on this issue, the causal mechanisms shaping accident severity remain insufficiently understood. This study applies Structural Equation Modeling (SEM) to quantify the influence of four latent constructs, Ship Characteristics, Environmental Conditions, Operational Risk, and Crew & Equipment, on inland maritime accident outcomes. Constructs were validated through principal component analysis, and the model achieved excellent fit (χ²/df = 1.363; RMSEA = 0.022; CFI = 0.94). Results show that Operational Risk (β = 0.84) and Crew & Equipment (β = 0.70) are the strongest predictors of severity. These insights highlight that the policy needs to strengthen operational oversight, crew training, and equipment standards. The proposed framework provides a transferable, evidence-based tool for enhancing safety governance in inland waterway transport both within China and globally.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:36:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667043</guid>
    </item>
    <item>
      <title>Optimal Load Balancing of Cooperative UAV-UGV Parcel Pickup to Minimize Completion Time</title>
      <link>https://trid.trb.org/View/2591335</link>
      <description><![CDATA[In this study, we investigate an optimal load balancing of cooperative parcel pickup between an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). By considering practical aspects, such as the movement characteristics of each vehicle and the avoidance of no-fly zones for the UAV, we aim to optimize the three-dimensional trajectories and pickup strategies of the two vehicles to identify the shortest route to minimize pickup completion time. To deal with the nonconvex optimization problem, we employ a successive convex approximation to convert the original problem into a convex form for optimization variables, and we also use a penalty convex-concave procedure to retain the binary natures of the control parameters that are required to design the pickup strategy. We also propose a two-stage iterative algorithm based on interior-point methods to solve the relaxed convex problem to find suboptimal solutions. The simulation results confirm that the proposed scheme can successfully allow the UAV and UGV to follow the shortest effective path and thus improve pickup completion time through load balancing, while outperforming the baseline schemes under various scenarios.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591335</guid>
    </item>
    <item>
      <title>Freight Footprints and Urban Heat Islands: Interactions between Freight Facilities, Built Environment and Thermal Consequences</title>
      <link>https://trid.trb.org/View/2648042</link>
      <description><![CDATA[Previous studies on urban heat island (UHI) and its influencing factors have focused on built environment and urban functional areas. However, freight facilities, large-scale facilities with impervious surfaces and high albedo characteristics widely used in the era of e-commerce, have not been fully examined in terms of their potential impacts on land surface temperature (LST). In this study, we applied satellite image datasets and machine learning techniques to identify freight facilities, and employed K-means clustering, multiple linear regression (MLR), and eXtreme Gradient Boosting (XGBoost) to investigate how different aggregation patterns of freight facilities, in conjunction with built environmental factors, differentially affect LST. The results show that this method can accurately capture the freight building facilities compared to other data forms. Second, freight facilities have a significant positive impact on LST; the existence of freight facilities will significantly increase the annual average LST by approximately 0.25°C, an effect that amplifies substantially to 0.393°C during summer. Subsequently, we categorized grids into three groups based on the scale, quantity, agglomeration level, and location of freight facilities, and validated the heterogeneous effects of their combination with built environment on LST. The warming effect of impervious surfaces is substantially amplified in freight facilities area, while ecological functions are suppressed or even nullified in highly concentrated area. Additionally, complex and non-linear relationships between the built environment and LST reflect across different freight clusters. This study provides actionable insights for planners and policymakers to develop freight facility planning strategies that prioritize ecological sustainability and long-term development.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:15:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648042</guid>
    </item>
    <item>
      <title>Smart logistics' spatial association network and green transformation of logistics industry–based on the perspective of industrial chain resilience</title>
      <link>https://trid.trb.org/View/2655796</link>
      <description><![CDATA[As a crucial link connecting production and consumption, the logistics industry is of paramount importance in national economic performance and high-quality development. Against the backdrop of “dual carbon” goals, achieving the synergistic improvement of economic and green benefits in the logistics industry has become an urgent task. This study, based on China's logistics industry panel data from 2013 to 2023, constructs a regional smart logistics (SLs) spatial association network and measures the green total factor productivity (GTFP) of the logistics industry, to systematically examine the interaction mechanisms and influencing relationships between the two. The study finds that China's SLs network is becoming increasingly interconnected, showing obvious “core-periphery” structural characteristics, with core cities playing a significant radiating and driving role in promoting the intelligent transformation of less developed regions. Further empirical analysis indicates that the spatial association network of SLs can significantly promote the improvement of regional logistics GTFP. The mechanism inspection results show that this promotion effect is mainly achieved by enhancing industrial chain resilience, especially the improvement of defensive resilience and recovery resilience is the most significant. Heterogeneity analysis further demonstrates that this green enabling effect is more prominent in the eastern region, regions with lower levels of economic growth, and regions with stricter environmental governance constraints. This not only provides new empirical evidence for the inclusiveness and government governance role in regional digital development, but also provides useful references for other developing countries to improve logistics efficiency and promote green transformation.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:15:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655796</guid>
    </item>
    <item>
      <title>Potential Austrian user groups for new cargo bike services</title>
      <link>https://trid.trb.org/View/2655693</link>
      <description><![CDATA[Despite being a niche market, cargo bikes have significant potential to revolutionize urban mobility. Nevertheless, their adoption for personal use remains limited, with usage patterns concentrated among highly educated urban men aged 30–45 in German-speaking countries. This study aimed to explore potential user groups for cargo bikes and respective mobility services by applying an explorative segmentation approach. The research focused on understanding potential cargo bike users’ socio-demographic, socio-economic, and socio-spatial characteristics, along with their current mobility behaviors and attitudes. Therefore, an original survey of 1,511 participants was conducted across Austria, capturing various variables: travel behavior, mobility preferences, attitudes, and perceived barriers to cargo bike usage. The methodological approach involved CHAID and TwoStep cluster analysis. It resulted in the identification of six clusters with moderate quality, of which three can be interpreted as potential cargo bike user groups: (1) cargo bike-affine urban dwellers, (2) bike-affine best agers, and (3) younger multimodal urban dwellers with neutral-to-positive attitudes, offering potential to prevent car dependency. These groups show favourable conditions based on current mobility behavior and attitudes for cargo bike uptake, each group in different stages of the Stage Model of Self-Regulated Behavioural Change [Bamberg, Journal of Environmental Psychology 34, 2013]. Following behavioral change theories, encouraging cargo bike use will require a multi-level strategy addressing the specific needs of each stage, combining general awareness campaigns with targeted interventions. This study enhances the understanding of user segmentation and supports effective promotion of sustainable mobility]. Following behavioral change theories, encouraging cargo bike use will require a multi-level strategy addressing the specific needs of each stage, combining general awareness campaigns with targeted interventions. This study enhances the understanding of user segmentation and supports effective promotion of sustainable mobility.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:15:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655693</guid>
    </item>
    <item>
      <title>Development of Systemic Large Truck Safety Analyses</title>
      <link>https://trid.trb.org/View/2680726</link>
      <description><![CDATA[Texas has the highest number of fatal crashes involving large trucks in the US since 1994, and this number in 2012 grew by 82% from 299 crashes in 2009. Due to the size and weight, crashes involving large trucks are usually more destructive, and therefore are a major health and safety concern for Texans. Studies are needed to better understand the risk factors related to large truck crashes and identify effective countermeasures to reduce crashes involving large trucks. The goals of this research are to analyze the risk factors of large truck involved crashes, recommend low-cost, high effective countermeasures, as well as determine about how many large truck crashes can be reduced by specific countermeasures implementation. To achieve the research goals, the research team (1) conducted crash data analysis to identify the crash hot spots and contributing factors to the large truck involved crashes; (2) conducted risk assessment in order to prioritize the risk factors; (3) surveyed truck drivers to validate the identified crash risk factors; (4) identified potential effective countermeasures for preventing large truck-involved crashes; and (5) conducted cost benefits analysis and recommended the most cost-effective countermeasures. Finally, 14 crash risk factors related to roadway conditions, traffic control, drivers and vehicle characteristics were identified; and 24 cost-effective safety countermeasures related to traffic engineering, traffic law enforcement, road user education, and emergency response were identified, and their costs and benefits were analyzed.]]></description>
      <pubDate>Mon, 30 Mar 2026 14:05:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680726</guid>
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