<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>A decomposition approach for holistic vessel traffic service planning in seaport waters</title>
      <link>https://trid.trb.org/View/2742979</link>
      <description><![CDATA[Efficient management of vessel traffic in seaports depends on three closely linked decisions: vessel sequencing, pilot assignment, and tugboat allocation. Traditionally, these decisions are addressed sequentially, resulting in suboptimal or even infeasible vessel traffic service plans. In this study, we consider a joint vessel sequencing, pilot assignment, and tugboat allocation problem (VSPTAP), in which a seaport manages a diverse team of pilots and a heterogeneous fleet of tugboats to service inbound and outbound vessels. We propose a mixed-integer linear programming model for the VSPTAP, which captures the multi-segment navigation structure, channel restrictions, diverse pilotage and tugboat requirements, and the interactions among the three decision layers, with the objective of minimizing the total delay cost of all vessels. While general-purpose solvers suffice for small-scale instances, we propose a decomposition approach to efficiently handle larger and more practical scenarios. The approach partitions the VSPTAP into a master vessel sequencing problem, together with two subproblems for pilot assignment and tugboat allocation, solved via reinforcement learning, column generation, and column enumeration, respectively. These methods are integrated within a hybrid optimization framework that dynamically coordinates vessel sequencing with pilotage and tugboat schedules. Extensive computational experiments based on Qinzhou Port, China, show that the proposed approach substantially outperforms the benchmark methods from the literature and current practices, yielding satisfactory solutions within 15 minutes and enabling daily cost savings of about RMB 232 thousand for the seaport. Furthermore, managerial insights are offered to support the operations and management of seaport traffic.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742979</guid>
    </item>
    <item>
      <title>A dynamic saturation framework for operational state assessment and sustainable vessel traffic management in tidally influenced river reaches</title>
      <link>https://trid.trb.org/View/2734399</link>
      <description><![CDATA[Safe and sustainable operation of maritime traffic systems requires timely identification of overload before congestion and safety risks emerge. Tidally influenced river reaches are particularly vulnerable because intense traffic demand, dynamic environmental constraints, and heterogeneous vessel maneuvers jointly strain limited navigable resources. To address this challenge, this study proposes a dynamic saturation-based framework for operational health monitoring using AIS trajectories and spatio-temporal consumption theory. By calculating the ratio of spatio-temporal resources consumed by microscopic vessel maneuvers—including longitudinal navigation, crossing, merging, exiting, and turning—to the total available spatio-temporal capacity, the framework establishes a unified dynamic saturation indicator for quantifying real-time waterway resource utilization. An empirical investigation of the Taicang section of the Yangtze River is conducted to validate the proposed framework. The relationships between dynamic saturation and key macroscopic traffic variables, including traffic volume, speed dispersion, time headway, and vessel traffic degrees of freedom, are systematically examined. The results identify four operational states: non-following free navigation, partial-following interaction, near-equilibrium collaborative navigation, and oversaturated blocked queuing. These states are directly mapped to a four-tier VTS intervention scheme, providing interpretable thresholds for graded intervention, early warning, preventive control, and sustainable vessel traffic management of limited spatio-temporal waterway resources.]]></description>
      <pubDate>Tue, 11 Aug 2026 16:35:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2734399</guid>
    </item>
    <item>
      <title>Orientation-aware dynamic vision-AIS association for UAV-based maritime surveillance</title>
      <link>https://trid.trb.org/View/2699405</link>
      <description><![CDATA[As maritime traffic continues to intensify, conventional surveillance systems are increasingly challenged to deliver both real-time responsiveness and high detection accuracy. Heterogeneous sensor fusion presents a promising solution to this challenge, yet it remains limited by persistent technical hurdles, including cross-modal data association and feature-level fusion. An orientation-aware dynamic vision-AIS association method for unmanned aerial vehicle (UAV)-based maritime surveillance is proposed. To achieve it, an orientation-aware object detection network is developed to simultaneously extract surface vessel position and heading from UAV vision, and multi-phase angle encoding-decoding strategy is applied to resolve angular discontinuity. Global orientation can then be estimated from visual data via camera projection model and coordinate transformations. An orientation based dynamic association method is proposed, where a similarity matrix jointly modeling positional and heading consistency is adaptively weighted to accommodate varying traffic scenarios. To further enhance robustness and computational efficiency, spatial hashing is employed to eliminate implausible association candidates, while Multiple Hypothesis Tracking (MHT) is incorporated to resolve ambiguities arising from occlusions, noise, and temporal asynchrony. A specialized dataset 360MariDrone comprising thousands of synchronized visual and AIS samples collected by UAV under diverse weather conditions is constructed. Quantitative experiments have been carried out on personal and public datasets, and an overall association accuracy of 85.35% is achieved, illustrating the effectiveness for heterogeneous data fusion in maritime surveillance.]]></description>
      <pubDate>Tue, 16 Jun 2026 11:38:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2699405</guid>
    </item>
    <item>
      <title>Long-horizon vessel trajectory prediction with step-wise traffic-pattern priors and sequential learning</title>
      <link>https://trid.trb.org/View/2702281</link>
      <description><![CDATA[Reliable long-horizon vessel trajectory forecasting is essential for Vessel Traffic Services (VTS) to support early traffic organization and safety monitoring. However, purely data-driven multi-step predictors often produce operationally implausible forecasts, which can undermine trust in strategic look-ahead warnings. This paper proposes a traffic-pattern-informed framework that continuously conditions long-horizon forecasting on local historical Speed-Over-Ground and Course-Over-Ground distributions extracted around the current vessel state, so as to encourage more plausible step-wise predictions. In addition, a sequential learning strategy with horizon-specific predictors is employed to improve long-horizon stability by conditioning each step on previously predicted state and updated local patterns. Experiments on two real-world AIS datasets (US Coast Guard and Danish Maritime Authority) show consistent improvements over representative baselines, reducing cumulative displacement error by up to 53.9%, with notable gains in constrained scenarios such as port approaches. The proposed framework is suited for strategic look-ahead in VTS, where stable long-horizon forecasts are required to support early conflict screening and traffic organization.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:56:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702281</guid>
    </item>
    <item>
      <title>Innovating Waterway Route Planning as a Service for Marine Traffic Applications</title>
      <link>https://trid.trb.org/View/2659147</link>
      <description><![CDATA[Waterway pattern mining and route planning are essential system services in maritime applications to support safety, efficiency, and sustainability goals. In this paper, first, we propose a novel waterway pattern mining method. It allows a compact footprint design and is featured with manifold granularities, waypoint and directional tagging, forming a knowledge base with rich traffic features. Second, relying on the extracted waterway patterns as “map context”, we enhance the Theta* path planning algorithm by taking the extracted traffic properties into consideration. An integrated solution combining both the waterway patterns and the enhanced Theta* algorithm has been applied to several domain use cases: a) waterway pattern based trajectory reconstruction; b) passage plan generation for various types of vessels; and c) vessel movement estimation and forecasting. These use cases proved the usefulness and practicality of the proposed solution, its feasibility for other port waters, and potential use on a global scale.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659147</guid>
    </item>
    <item>
      <title>A novel maritime traffic complexity model based on ship trajectory prediction</title>
      <link>https://trid.trb.org/View/2693608</link>
      <description><![CDATA[As global shipping density increases, maritime traffic in complex waterways exhibits pronounced dynamic and multi-scale characteristics, while traditional static assessments struggle to support proactive risk warning. To address this gap, this study proposes a novel, unified framework that integrates prediction, clustering, micro-level complexity quantification, and Shapley-based cross-scale fusion into a single methodology. First, a Bidirectional Gated Recurrent Unit Sequence to Sequence (Bi-GRU Seq2Seq) model with a fusion attention mechanism is constructed to achieve short term vessel trajectory prediction based on AIS data. Second, predicted trajectories are clustered using the density based spatial clustering of applications with noise algorithm to identify potential encounter clusters. Third, a micro level complexity model is developed from four dimensions, namely motion trend, relative distance, distance at closest point of approach, and relative bearing, to quantify vessel interaction risk. Finally, Shapley value theory is utilized to realise nonlinear cross scale fusion from vessels to clusters and to the regional level, enabling marginal contribution analysis and interpretable macro level assessment. A case study in the Laotieshan Channel of the Bohai Sea demonstrates that the prediction model achieves a Root Mean Square Error of 84.99 m, with a maximum error reduction of 76 per cent compared with baseline models. The proposed framework effectively identifies risk hotspots and captures the aggregation and diffusion patterns of traffic complexity. By enabling a transition from static assessment to proactive risk warning, this study provides a quantifiable and interpretable methodological basis for intelligent maritime supervision, refined vessel traffic services management, and proactive traffic risk warning.]]></description>
      <pubDate>Thu, 23 Apr 2026 09:39:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693608</guid>
    </item>
    <item>
      <title>Distributed MPC for autonomous ships on inland waterways with collaborative collision avoidance</title>
      <link>https://trid.trb.org/View/2676767</link>
      <description><![CDATA[This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for modifying traffic rules without changing the collision avoidance algorithm, which is based on a novel formulation of model predictive control (MPC) for collision avoidance of ships. This MPC formulation is designed for inland waterway traffic and can handle complex scenarios. The alternating direction method of multipliers (ADMM) is used as a scheme for exchanging and negotiating intentions among ships. Simulation results demonstrate that the proposed algorithm enables ships to avoid collisions with a sufficient margin while adhering to traffic rules. Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase traffic efficiency in complex scenarios.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676767</guid>
    </item>
    <item>
      <title>Mitigating container port congestion and carbon emissions through AI and capacity sharing</title>
      <link>https://trid.trb.org/View/2670101</link>
      <description><![CDATA[Container port congestion results in substantial economic losses and carbon emissions. To examine the effectiveness and interaction of two common congestion mitigation strategies, capacity sharing and artificial intelligence (AI) investment, this study develops a game-theoretical model of two competing ports (a dominant port and a non-dominant port), and explores their impacts on carbon emissions. The results show that capacity sharing can alleviate the non-dominant port’s congestion by transferring service volume to the dominant port. This process reduces the non-dominant port’s carbon emission but increases those of the dominant port, and total emissions may rise when the non-dominant port’s capacity is relatively low. In contrast, AI investment consistently alleviates congestion and reduces carbon emissions. However, when the two ports have similar capacities, they may fall into a prisoner’s dilemma in their AI investment decisions. Moreover, we identify the capacity and cost thresholds for the non-dominant port’s optimal congestion mitigation strategy.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670101</guid>
    </item>
    <item>
      <title>Intelligent traffic organization for sea ports: Fusing multi-source data for resource allocation and scheduling</title>
      <link>https://trid.trb.org/View/2682207</link>
      <description><![CDATA[Reliable autonomous traffic organization is critical for smart ports operating under dynamic ocean conditions. This study proposes a comprehensive framework for sea port scheduling based on Multi-Source Information Fusion (MSIF). To address the challenge of fusing heterogeneous data streams-including vessel trajectories, resource availability, and environmental constraints-a Mixed-Integer Programming (MIP) formulation is first established. Subsequently, a high-fidelity digital twin integrating Cellular Automata (CA) and Multi-Agent Systems (MAS) is developed to simulate heterogeneous cooperative behaviours and stochastic uncertainties. Crucially, a novel Simulation-based Multi-Objective Genetic Algorithm (SMOGA) serves as the autonomous decision-making engine, fusing simulation feedback with evolutionary search to optimize vessel sequencing and resource allocation dynamically. Validated with real-world data from Tianjin Port, the framework outperforms both traditional rules and modern swarm intelligence algorithms, increasing throughput by 16.5% and reducing turnaround time by 11.7%. This research demonstrates how fusing multi-scale information into an intelligent evolutionary framework enables robust, autonomous decision-making in congested maritime environments.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682207</guid>
    </item>
    <item>
      <title>A systematic review of cognitive and social factors in vessel traffic services operations</title>
      <link>https://trid.trb.org/View/2673077</link>
      <description><![CDATA[Vessel traffic service (VTS) plays a key role in the safety of maritime navigation by organising the sea traffic, ensuring regulatory compliance, promoting information exchange and early detection of navigational hazards and assisting in collision avoidance. The cognitive and social factors influencing the performance of VTS operators require important considerations in this regard. Current developments in the maritime industry and changing operational profiles present novel challenges for VTS operators. This study aims to present the empirical findings related to the applied cognitive and social factors pertaining to VTS operations for the past two decades. A systematic literature review was conducted with a Boolean search strategy across six major databases. The literature associated with empirical investigations was extracted as per the PRISMA guidelines. The study identified 19 articles that satisfied the pre-determined inclusion criteria. A qualitative synthesis of the identified literature was performed, aggregating the findings into various sub-groups based on thematic areas and contexts. The obtained results revealed fatigue and mental workload as the most frequently examined factors, while factors such as decision-making, communication, coordination and perception also influenced the VTS operator’s performance. The findings shed light on the current state of the art for research and practical applications related to cognitive and social factors influencing VTS operator performance and their impact on maritime safety. The result also identified gaps in the literature where further research is warranted, particularly related to emerging trends of automation and digitalisation in the maritime industry.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:44:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673077</guid>
    </item>
    <item>
      <title>Energy efficient formation control of multi vessel systems via hydrodynamics aware configuration optimization</title>
      <link>https://trid.trb.org/View/2655815</link>
      <description><![CDATA[Existing studies on multi-vessel formations rarely combine physically based models of ship–ship hydrodynamic interaction with online formation control, so that energy benefits are typically assessed offline or only approximated through artificial potentials. This paper addresses this gap by embedding a reduced-order, hydrodynamics-aware resistance model into a hierarchical formation control framework for multi vessel systems. A three degree of freedom interaction model is incorporated into the cost function, enabling the supervisory controller to adaptively optimize inter ship spacing and formation geometry in a speed dependent and hydrodynamics aware manner. The lower level MPC ensures accurate trajectory tracking and stability under the guidance of the top level optimization. Four simulation studies are conducted to evaluate the proposed method. The platooning formation is first analyzed as a reference, followed by the triangular formation, which achieves balanced tracking performance and stability. The echelon formation is then examined, demonstrating significant energy savings in medium to high speed regimes while maintaining yaw stability. Finally, an unconstrained optimization scenario is explored, where the system autonomously adapts its geometry without prescribed patterns, revealing emergent energy efficient and stable arrangements across different speed ranges. Results show that the proposed approach not only reduces resistance and improves energy efficiency but also enhances formation adaptability and robustness under varying operating conditions. These findings provide new insights into hydrodynamics aware cooperative control and the development of energy conscious fleet management strategies for future maritime transportation.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:55:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655815</guid>
    </item>
    <item>
      <title>Analysis of the effects of berth characteristics on operational performance of Nigerian seaports</title>
      <link>https://trid.trb.org/View/2617647</link>
      <description><![CDATA[Nigerian seaports are pivotal to the country’s economic progress, but their efficiency is hindered by inadequate berthing characteristics. This study examines how various berthing factors such as the number of berths, quay length, maximum depth, operational areas, and cargo-specific zones affect the performance of four major Nigerian seaports; Tin Can Island, Apapa, Onne, and Delta. Primary and secondary were utilised. Using the Taro Yamane formula, a sample size of 380 were calculated, and 367 valid questionnaire responses analyzed. The secondary data on the berth-characteristics were obtained from the Nigerian Ports Authority Abstract. Data analysis combined descriptive statistics with inferential methods using SPSS, such as coefficients, ANOVA, and model summary, with results presented in tables. The findings show that while the existing number of berths largely meets traffic demands, congestion occurred during peak periods, highlighting capacity limitations. Quay length was identified as a major determinant of efficiency, longer quay enhances vessel handling and reduce delay. Maximum berth depth was adequate for most operations but emphasized the need for deeper berths to handle larger vessels with high draft requirements. Regression analysis revealed that berth depth, quay length, operational area, management style, dedicated areas, investment in berth infrastructure, and number of berths collectively explained 65.2% of the variability in performance of seaport. The study concludes that enhancing berthing characteristics significantly improve the operational efficiency. Strategic investments to increase berth numbers, extend quay lengths, deepen berths, and create dedicated cargo zones to ease congestion, improve vessel turnaround, and align Nigerian seaports with international standards.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:53:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617647</guid>
    </item>
    <item>
      <title>Kalman-Filter-Based Vessel Position Prediction for Isolated Island Routes with Long Sampling Intervals</title>
      <link>https://trid.trb.org/View/2598683</link>
      <description><![CDATA[In this study, the authors are aiming for predicting and imputation missing data when they use Kalman Filter for estimating vessel position. When dealing with an isolated island sea route like this one, the sampling interval for data acquisition may be long due to problems with equipment, etc. In this case, the authors would only use state extrapolation. In such case, when predicting the next position data, the accuracy of the prediction step alone will be greatly reduced if the sampling interval is long. Then, in this paper, the authors propose vessel position prediction method that uses sea route predefined for a vessel, and report on the improvement in the accuracy of position prediction under certain conditions.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598683</guid>
    </item>
    <item>
      <title>Ship trajectory prediction under multi-ship coupling effect based on a generative adversarial network</title>
      <link>https://trid.trb.org/View/2605004</link>
      <description><![CDATA[Ship trajectory prediction is among the key technologies used for maritime traffic management to avoid collisions and guarantee navigation safety. However, the complicated coupling effect resulting from ship maneuvering behaviors during multi-ship encounters is usually ignored and difficult to quantify reasonably. Therefore, the authors propose a novel ship trajectory prediction framework based on generative adversarial networks with a multilayer perceptron (MLP) and a multi-head probsparse self-attention mechanism (GAN-MM) to predict ship trajectories under the coupled effect of multiple ships. The method uses the distance to closest point of approach (DCPA) and the time to closest point of approach (TCPA) to determine the neighboring ships affecting the target ship. On this basis, the MLP is adopted to quantify the multi-ship coupling effect caused by multi-ship encounters to extract the potential navigation feature vector of the target ship. Then, bidirectional long short-term neural network (Bi-LSTM) is employed as the encoder and decoder in the generator to capture the bidirectional time series features of the trajectories. Moreover, a multi-head probsparse self-attention mechanism is embedded between the encoder and decoder to efficiently mine correlations among hidden representations. Finally, a discriminator based on Bi-LSTM and an MLP is adopted to determine the authenticity of the predicted trajectory to evaluate the performance and accuracy of the proposed framework. To verify the effectiveness of the proposed framework, numerical experiments are conducted. The experimental results show that the proposed GAN-MM is able to quantify the coupling effect caused by ship maneuvering behaviors under multi-ship encounters and greatly improve prediction accuracy. The comparison experiments also indicate that the GAN-MM is obviously superior to the other comparison methods.]]></description>
      <pubDate>Mon, 13 Oct 2025 08:49:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2605004</guid>
    </item>
    <item>
      <title>Thermographic and cognitive assessment of fatigue in vessel traffic service operators: a socio-technical approach</title>
      <link>https://trid.trb.org/View/2573038</link>
      <description><![CDATA[Fatigue among vessel traffic service operators (VTSOs) poses critical risks to maritime safety, requiring objective, real-time monitoring solutions. This study integrates thermographic imaging and subjective self-reports to assess fatigue and mental workload in operational environments. A total of 23 VTSOs from two Spanish Maritime Rescue Coordination Centers (MRCCs), with an average of 7.74 years of experience, were observed for 200 h. Using facial thermography, specifically nasal temperature variations, and validated psychological assessments, we analyzed the physiological and cognitive effects of shift work. Results show that night shifts significantly increase fatigue, with a 15% increase in perceived exertion and a 2 °C decrease in nasal temperature. Automated face recognition via YOLO5Face facilitated real-time thermographic analysis, improving the accuracy of fatigue monitoring. Thermographic imaging successfully correlated nasal temperature changes with cognitive workload, demonstrating its potential as a non-invasive tool for fatigue assessment. In addition, pre-task rest was inversely related to fatigue, highlighting the importance of rest management in mitigating operator fatigue. By bridging cognitive systems engineering and socio-technical perspectives, this study provides a novel framework for fatigue assessment in safety–critical environments. The results support the integration of thermographic methods into maritime traffic management, contributing to human-centered safety technologies. Future research will explore broader applications of automated thermal analysis and additional physiological fatigue markers in high-risk industries.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2573038</guid>
    </item>
  </channel>
</rss>