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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>Free-running manoeuvring experiments of a fishing vessel model: Effects of rudder geometry and hull appendages</title>
      <link>https://trid.trb.org/View/2725555</link>
      <description><![CDATA[This paper presents a physical modelling study investigating the influence of hull appendages and propeller-rudder configurations on the manoeuvring performance of a representative North Atlantic fishing vessel. A 2.5-m free-running, remotely controlled scale model of the fishing vessel was instrumented with motion, acceleration, and force sensors and equipped with integrated propulsion and steering systems, synchronized via a dedicated data acquisition framework. Manoeuvring experiments were conducted in accordance with ITTC guidelines and comprised turning-circle, zigzag, pull-out, and crash-stop trials. The results show that both hull appendages and rudder configuration significantly influence vessel manoeuvrability. In general, the presence of appendages enhanced turning performance, while the single-foil rudder yielded additional improvements relative to alternative flat-plate rudder arrangements. Repeatability analyses confirm the robustness of the experimental measurements. The findings provide new experimental insights into the coupled hydrodynamic interactions among the hull, propeller, rudder, and appendages, thereby supporting improved prediction accuracy in manoeuvring performance assessments.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725555</guid>
    </item>
    <item>
      <title>Vessel-risk-aware: a decision support model for vessel routing based on multicriteria decision analysis and an advanced Dijkstra algorithm</title>
      <link>https://trid.trb.org/View/2709466</link>
      <description><![CDATA[Maritime weather routing research has largely prioritized minimizing operating costs, fuel consumption, and estimated time of arrival (ETA). However, existing models often neglect accident avoidance and environmental risks. Fishing vessels particularly face many challenges from dynamic coastal hazards, frequent operational stops, and diverse safety thresholds. This study proposes a multicriteria spatial decision support system (SDSS) that integrates an enhanced Dijkstra algorithm with Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to address these gaps. The framework advances maritime navigation through three major contributions. First, it incorporates eight-directional pathfinding into the Dijkstra algorithm to better account for coastal navigation constraints. Second, it integrates dynamic vulnerability indices derived from bathymetry, wave conditions, and vessel speed, calibrated against historical accident data to reflect real-world risk. Third, it provides an interactive interface that enables stakeholders to assign relative importance to safety, efficiency, and environmental criteria, thereby fostering transparent and collaborative decision-making. Together, these innovations generate optimized routes that balance multiple objectives under varying oceanic conditions such as wind, waves, and currents. By bridging theoretical routing models with the practical demands of fisheries management, this framework offers a scalable tool for safer maritime navigation in weather-dependent contexts.]]></description>
      <pubDate>Tue, 30 Jun 2026 08:51:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709466</guid>
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    <item>
      <title>Marine Investigation Report: Flooding and Sinking of Fishing Vessel North American, May 14, 2024</title>
      <link>https://trid.trb.org/View/2709194</link>
      <description><![CDATA[On May 14, 2024, about 0550 local time, vessel representatives for the fishing vessel North American, which was uncrewed and docked on the Lake Washington Ship Canal, in Seattle, Washington, were notified that the vessel was flooding. Salvors arrived on scene and attempted to dewater the vessel but were unable to keep up with the flooding, and the vessel eventually sank. There were no injuries, and no pollution was reported. Damage to the vessel was initially estimated at $3 million. The National Transportation Safety Board determines that the probable cause of the flooding and sinking of the fishing vessel North American was hull corrosion, which resulted in wastage holes that allowed water ingress into the engine room overnight while the vessel was unattended.]]></description>
      <pubDate>Thu, 11 Jun 2026 13:20:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709194</guid>
    </item>
    <item>
      <title>Temporal Instability Analysis of Fatal Commercial Fishing Vessel Incidents: A Correlated Random-Parameter Model with Heterogeneity in Means</title>
      <link>https://trid.trb.org/View/2670338</link>
      <description><![CDATA[Fatal commercial fishing vessel incidents remain a critical global safety challenge, yet empirical understanding of their underlying determinants is limited by strong unobserved heterogeneity, correlated risk mechanisms, and temporal instability in covariate effects. This study examines how the influence of contributory factors has changed over time using 23 years of data from the U.S. Commercial Fishing Incident Database. A correlated random-parameter logit model with heterogeneity in means is developed to capture unobserved heterogeneity, parameter correlation, and context-dependent variability in risk effects. Temporal instability is assessed through both global and pairwise likelihood ratio tests across five sub-periods. The results demonstrate significant temporal non-stationarity. Weather-related conditions and the absence of a mayday call consistently increase fatality risk across multiple periods. In earlier years, capsizing events and human factors were more influential, reflecting the prominent role of vessel stability and crew performance in early-stage incident outcomes. The use of an EPIRB to send a mayday signal appears as a random parameter in several periods, and its effect varies with ship age, weather conditions, and struck events, indicating that latent operational and behavioral factors shape its effectiveness. Overall, the proposed model achieves superior statistical fitting, improved interpretability, and richer behavioral insights compared with fixed-parameter or standard random-parameter models. The findings highlight the need for time-sensitive and risk-adaptive safety interventions, with recommendations to strengthen communication reliability, emergency preparedness, and context-specific safety management in the commercial fishing sector.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670338</guid>
    </item>
    <item>
      <title>Big Data Analytics for Weather Prediction Integrating Regression and ARIMA Models to Assess the Impact of Climate Variability on Fishermen Safety and Maritime Operations</title>
      <link>https://trid.trb.org/View/2655544</link>
      <description><![CDATA[Objectives: This study aims to develop an AI-based early warning system for maritime navigation by integrating machine learning techniques to predict weather conditions and assess navigation risks. The research focuses on improving forecasting accuracy for key meteorological and oceanographic variables to enhance navigational safety. Theoretical Framework: The study is grounded in predictive analytics and artificial intelligence applications in maritime risk assessment. It leverages machine learning models, including ARIMA, Random Forest, SVM, and Artificial Neural Networks, to enhance the accuracy of weather and sea condition forecasts, providing valuable insights for maritime operations. Method: The research employs a data-driven approach, utilizing historical meteorological and oceanographic data to train and evaluate machine learning models. Variables such as air temperature, wind speed, sea temperature, rainfall, and air pressure are analyzed using regression, time-series analysis, and statistical modeling techniques to develop an effective predictive system. Results and Discussion: The findings reveal that AI models, particularly ARIMA and regression analysis, demonstrate high predictive capability for air temperature variations. However, dataset limitations and model parameter tuning impact accuracy. The results highlight the importance of selecting appropriate variables and optimizing model structures to improve forecasting reliability. Research Implications: The study contributes to maritime safety by providing a framework for real-time weather forecasting and risk assessment. The findings can inform decision-making in vessel operations and policy development for maritime safety regulations. Originality/Value: This research integrates AI and predictive analytics to enhance maritime navigation safety, addressing gaps in real-time risk assessment and forecasting. The proposed framework provides a foundation for further advancements in AI-driven maritime decision support systems.]]></description>
      <pubDate>Thu, 14 May 2026 17:05:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655544</guid>
    </item>
    <item>
      <title>HDFormer: A transformer-based model for fishing vessel trajectory prediction via multi-source data fusion</title>
      <link>https://trid.trb.org/View/2660818</link>
      <description><![CDATA[Predicting the trajectory of fishing vessels is essential for enhancing navigational safety and aiding fishery management. Compared to other vessels, trajectory prediction for fishing vessels presents a unique challenge as these vessels exhibit distinct movement patterns; they often make frequent turns while fishing and follow straight paths during steaming. This study introduces HDFormer, a Transformer-based deep learning model engineered to predict the future trajectories of fishing vessels up to one and a half hours in advance. HDFormer utilizes two innovative attention mechanisms – Trajectory Attention and Environment Attention – that integrate spatial features from historical trajectory segments, fishing effort distributions, and hydrological factor fields. These mechanisms help clarify the interactions among these elements, offering insights into potential future operational states. Tested with the VMS dataset of 418 seiners in the East China Sea and hydrological data from the Copernicus Climate Data Store, HDFormer achieves a mean absolute error of 0.773 nautical miles and a final displacement error of 1.642 nautical miles. HDFormer is readily adaptable to other oceanic regions for long-term trajectory forecasting, and its innovative Environmental Attention mechanism has broad potential applications in fishing research.]]></description>
      <pubDate>Thu, 23 Apr 2026 09:12:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2660818</guid>
    </item>
    <item>
      <title>Fundamental Study on Maneuver Evaluation of Fishing Vessels for Developing Collision Avoidance Algorithms in Autonomous Ships</title>
      <link>https://trid.trb.org/View/2669605</link>
      <description><![CDATA[Research on autonomous ships has been progressing rapidly in recent years, aiming to enhance safety and efficiency in marine transportation. However, collision avoidance with fishing vessels remains a particularly difficult challenge for autonomous navigation algorithms because of their irregular movements and diverse operating patterns. This study focuses on evaluating the maneuvering behavior of a powered vessel encountering fishing vessels. Simulator-based experiments were conducted using multiple encounter scenarios that reflected actual fishing activities, and avoidance maneuvers were analyzed based on the responses of student participants. The results and expert evaluations were compared to identify tendencies in appropriate and inappropriate avoidance behaviors. These findings are expected to provide fundamental insights for establishing evaluation criteria and improving future collision-avoidance algorithms for autonomous ships.]]></description>
      <pubDate>Mon, 20 Apr 2026 11:14:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669605</guid>
    </item>
    <item>
      <title>A-NetShoot: Adaptive vessel guidance for purse seine net shooting</title>
      <link>https://trid.trb.org/View/2656522</link>
      <description><![CDATA[Purse seining captures large pelagic fish schools by shooting a net from a vessel that unfurls vertically to form a cylindrical wall around the fish. During the shooting phase, precise vessel guidance is crucial, especially against unpredictable currents and evasive, free-swimming fish. These factors are difficult to predict and are traditionally left to the discretion of experienced captains. As the industry moves toward intelligent systems, explicit planning is required to improve success rates and reduce operational costs. This paper frames net shooting as the deployment of an underactuated, length-increasing deformable linear object and introduces A-NetShoot, an optimization-based adaptive path planning framework for vessel guidance. A-NetShoot operates in two steps: first, it estimates the evolving net shape by tracking sparse buoy-mounted sensors and solving for the discretized net geometry via an online least-squares problem. In parallel, sonar observations are modeled with a Gaussian mixture to estimate fish school location and distribution. Using these estimates, the algorithm forecasts net drift and fish motion over time. Then, it refines the vessel trajectory to maximize enclosure success. Extensive simulations under varied currents and fish behaviors show that A-NetShoot outperforms state-of-the-art methods, demonstrating the potential of adaptive guidance based on net-geometry feedback.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:40:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656522</guid>
    </item>
    <item>
      <title>The influence of dihedral bulbous bows on the resistance of small fishing vessels: A numerical study</title>
      <link>https://trid.trb.org/View/2688356</link>
      <description><![CDATA[Environmental aspects in the shipping industry are nowadays taking more relevance. Independently of the type of fuel used, good drawing lines in ships might help with the emission mitigation and also with the ship efficiency. In the fishing industry, ships lines in non-developed countries and in small traditional ships are normally not optimized what leads to a no optimal use of the resources and operation. In this work this topic is treated. The lines of two fishing vessels are studied numerically and compared with towing tank experiments. Those lines from a displacement and a semi-displacement hull, are optimized by adding a new type of bow named as dihedral bulbous bow. This bow produces a reduction over 10% of the ship’s resistance. This work focuses on explaining numerically why that difference occurs. The bulbous bow reduces the pressure resistance by softening the flow that reaches the bow.]]></description>
      <pubDate>Tue, 07 Apr 2026 09:16:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688356</guid>
    </item>
    <item>
      <title>Marine Investigation Report: Grounding of Fishing Vessel Eileen Rita, April 11, 2025</title>
      <link>https://trid.trb.org/View/2684251</link>
      <description><![CDATA[​On April 11, 2025, at 0731 local time, the commercial fishing vessel Eileen Rita was transiting Massachusetts Bay when the vessel ran aground on Green Island, about 8 miles east of Boston, Massachusetts. The three crewmembers on board were rescued by local first responders. No injuries were reported. A diesel sheen was visible in the water after the grounding. The vessel later sank with an estimated 4,000 gallons of diesel fuel on board and was a constructive total loss valued at $720,000. The National Transportation Safety Board (NTSB) determined that the probable cause of the grounding of the fishing vessel Eileen Rita was the captain falling asleep while navigating the vessel due to fatigue resulting from an accumulated sleep debt and poor sleep quality in the preceding 48 hours.​​]]></description>
      <pubDate>Mon, 30 Mar 2026 08:55:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684251</guid>
    </item>
    <item>
      <title>Detecting anomalous behaviors in maritime vessel groups from radar images using a novel algorithm: Collective behavior inspired predictive algorithm (CeBiPA)</title>
      <link>https://trid.trb.org/View/2685340</link>
      <description><![CDATA[In the maritime region of coastal nations, fishing vessels are at the center of various security concerns, including smuggling, drug trafficking, and vessel attacks. However, accurately identifying such threats is challenging due to the complex, dynamic fishing vessel movements. The fishing vessels usually move together as groups using the information received by neighbor vessels. Similar behaviors can also be identified in animal groups, which are called collective behaviors. The vessels deviating from a group weaken the group’s collective behavior and can be identified as anomalous behavior. This study proposed a novel algorithm, Collective Behavior Inspired Predictive Algorithm (CeBiPA), by considering groups’ collective behaviors to predict the vessels’ next positions. The Long Short-Term Memory (LSTM) algorithm is used with CeBiPA to detect anomalous behaviors in groups. The method was evaluated on a vessel group dataset comprising 41 real and 61 simulated vessels derived from land-based navigational radar data, reflecting the limited availability of real data. The LSTM algorithm was trained with 9 features, including vessels’ predicted positions, and achieved 97.83% accuracy, with improvements of 2.12% in accuracy, 7.84% in F1-score, and 3.59% in Area under the Receiver Operating Characteristic Curve (ROC-AUC) compared to an LSTM algorithm trained without predicted positions.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:14:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685340</guid>
    </item>
    <item>
      <title>Numerical and experimental hydrodynamic assessment of a dihedral bulbous bow retrofit for a semi-planing fishing vessel</title>
      <link>https://trid.trb.org/View/2682161</link>
      <description><![CDATA[This study investigates the improvement in hydrodynamic performance achieved through the integration of a dihedral bulbous bow into a semi-planing fishing vessel, with particular emphasis on calm-water resistance and dynamic trim behavior. The results indicate a total resistance reduction of up to 28% under light-load condition and up to 32% under heavy-load condition.A dihedral bulbous bow is a developable surface bulbous bow appendage that partially pierces the free surface rather than being fully submerged. Both experimental towing tank tests and numerical simulations using computational fluid dynamics (CFD) are conducted to evaluate the retrofit effectiveness. The vessel examined is a traditional artisanal longliner fishing boat, as classified by the Food and Agriculture Organization (FAO), operating at high speeds with Froude numbers approaching 0.5, significantly higher than conventional displacement-type fishing vessels.The results emphasise the critical influence of dynamic lift forces on hull performance at elevated Froude numbers, where semi-planing behavior becomes dominant. Implementation of the dihedral bulbous bow demonstrates measurable improvements in hydrodynamic efficiency through favorable modifications to the wave pattern and pressure distribution around the hull. The combined effects of bow-generated lift, dynamic trim adjustment, and altered flow field contribute to reduced total resistance. Comparative analysis reveals distinct differences in the fluid dynamic behavior between the original hull and the retrofitted configuration, providing valuable insights for the design optimisation of high-speed fishing vessels.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682161</guid>
    </item>
    <item>
      <title>Marine Investigation Report: Fire aboard Fishing Vessel Spicy Lady, March 6, 2025</title>
      <link>https://trid.trb.org/View/2674280</link>
      <description><![CDATA[​On March 6, 2025, about 1320 local time, the commercial fishing vessel Spicy Lady was fishing in Chatham Strait about 1 mile west of Point Gardner, Alaska, when the vessel caught fire. The five crewmembers on board were unable to extinguish the fire and abandoned the vessel onto a nearby Good Samaritan vessel. The fire was later extinguished by responding firefighters. One crewmember suffered minor injuries. No pollution was reported. The vessel was declared a total constructive loss, valued at $1.6 million. The National Transportation Safety Board (NTSB) determined that the probable cause of the fire on board the fishing vessel Spicy Lady was an unknown source on a bunk in the forward berthing area.​​]]></description>
      <pubDate>Wed, 11 Mar 2026 14:18:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674280</guid>
    </item>
    <item>
      <title>Research on multi-condition configurational paths of collision accidents between merchant and fishing vessels in China's coastal waters</title>
      <link>https://trid.trb.org/View/2637934</link>
      <description><![CDATA[In China's coastal waters, the frequent entry of merchant vessels alongside large-scale fishing operations has led to recurring collisions that not only cause severe property losses but also often result in casualties. This study adopts a four-dimensional framework of “Human–Vessel–Management–Environment” and draws on 119 investigation reports of collision accidents between merchant and fishing vessels (CAMF) from 2014 to 2024. Seven antecedent conditions contributing to casualties were identified, and their causal mechanisms were examined using fuzzy-set qualitative comparative analysis (fsQCA). The exploratory analysis shows that these conditions can combine into two configurational paths leading to casualties with “unsafe behavior” consistently emerging as a core condition. In the confirmatory analysis, unsafe behavior is treated as a mediating variable between the other six antecedent conditions and casualties. Results reveal that these six conditions can generate five paths inducing unsafe behavior, which can be grouped into three categories. Moreover, in the absence of unsafe behavior, two additional configurational categories directly trigger casualties. These findings indicate that unsafe behavior plays a partial rather than full mediating role, underscoring the configurational diversity of accident causation. Finally, targeted governance measures are proposed to help reduce the risk of CAMF.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2637934</guid>
    </item>
    <item>
      <title>Towards a distinctive fishing ship domain: Integrating towed-gear effects, maneuverability constraints, and high-risk operational complexity</title>
      <link>https://trid.trb.org/View/2637898</link>
      <description><![CDATA[Fishing ships employing towed-gear are widely recognized as among the most collision-prone ship types owing to their operational complexity, reduced maneuverability during fishing phases, and inherently high accident-risk profile. In this study, the physical constraints of gear deployment and navigators’ perception of safety distances are integrated into a fishing ship domain model. A numerical analysis determined the range of minimum astern safety distances as 46.54–283.96 m, depending on ship draft and towing gear conditions. A boxplot analysis of perception-based survey data revealed that perceived safety distances increase during nighttime and active fishing operations. The derived domains are expressed as asymmetric elliptical functions with a more expanded astern sector than conventional merchant-ship-based models. The operational applicability of the model was validated through encounter cases based on automatic identification system data, where approaches within the proposed perceived domain corresponded to actual warning signals and avoidance maneuvers. By reflecting not only the effects of towed-gear but also the broader maneuverability constraints, operational complexity, and risk profile of fishing ships, the proposed fishing ship domain provides a more distinctive and realistic foundation for collision risk assessment, safety distance standards, and the integration of Maritime Autonomous Surface Ships in mixed traffic scenarios.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2637898</guid>
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