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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Optimizing alternative-fuel tugboat fleet towards decarbonization goals</title>
      <link>https://trid.trb.org/View/2694572</link>
      <description><![CDATA[As pivotal hubs in the global maritime supply chain, ports are under increasing pressure to decarbonize in response to tightening environmental regulations. Within port areas, heavy-duty tugboats, one of the main harbor craft, are major sources of port-area greenhouse gas emissions, yet systematic research for their decarbonization remains underdeveloped. Concurrently, growing global maritime traffic has amplified uncertainties in tugboat service demand, further complicating efforts to manage emissions. To address this gap, we develop a two-stage robust optimization model that determines an optimal tugboat fleet renewal plan considering six alternative fuels (biodiesel, liquefied natural gas, methanol, hydrogen, ammonia and electricity) under uncertain service demand and varying decarbonization targets. By integrating empirical operational performance, environmental impact, and economic viability, we aim to determine the fuel-mix renewal plan for the tugboat fleet that minimizes the fleet’s total costs, including capital costs, operational costs, and emission penalties. We employ an adapted column-and-constraint generation algorithm to solve this model. Through numerical experiments on real-world tugboat and vessel-call data from the Port of Singapore, our results reveal fuel-mix transition pathways aligned with different decarbonization milestones. We also quantify the influence of carbon prices on transitions in tugboat fuel types. The findings offer port authorities a decision-support tool for cost-effective, low-carbon, and operationally reliable planning for tugboat renewal.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694572</guid>
    </item>
    <item>
      <title>Integrated vessel–tugboat–pilot scheduling for emergency vessel evacuation in a multi-area port with a multi-segment channel</title>
      <link>https://trid.trb.org/View/2699392</link>
      <description><![CDATA[Extreme weather events such as typhoons pose severe risks to coastal ports, making the Emergency Vessel Evacuation Scheduling Problem (EVESP) crucial for protecting vessels and port infrastructure. Rapidly evacuating numerous vessels within a limited time window is particularly challenging in multi-area ports connected by multi-segment channels, where demand for tugboats, pilots, and unmooring teams surges, navigation rules are complex, and safety must be balanced against operational costs. This paper formulates the EVESP as a bi-objective mixed-integer linear programming model that minimizes evacuation time and tugboat costs. The model jointly optimizes vessel departure sequencing and timing and the assignment of heterogeneous tugboats and pilots, and incorporates adaptive tugboat strategies, including cross-area dispatching and short-term rental. To solve large-scale cases efficiently, we develop a Variable Neighborhood Search–enhanced NSGA-II with adaptive mechanisms to improve convergence and solution diversity. Experiments using Guangzhou Port as a representative application context show that the proposed approach generates high-quality Pareto solution sets within practical time limits. Further analyses quantify maximum evacuation workloads, identify when tugboat cross-area dispatching and rental are effective, and assess the benefits of augmenting pilots, unmooring teams, and channel capacity under emergency-planning settings. This paper offers actionable decision support for port emergency preparedness and response.]]></description>
      <pubDate>Tue, 16 Jun 2026 11:38:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2699392</guid>
    </item>
    <item>
      <title>CFD investigation of KVLCC2 flow distortion with IDDES at different drift angles and its impact on tugboat loads in ship maneuvering simulations</title>
      <link>https://trid.trb.org/View/2705021</link>
      <description><![CDATA[Ship maneuvering simulators (SMS) commonly represent currents as undisturbed fields, thereby neglecting the flow distortion generated by nearby vessels. This simplification may affect the estimation of tug loads during assisted maneuvers. The present work investigates the distorted flow field around a full-scale KVLCC2 tanker at drift angles from 0° to −90° in shallow water (h/T = 1.2) using the high-fidelity IDDES turbulence model and evaluates its influence on tugboat loads in maneuvering simulations. The CFD results reveal two distinct flow regimes: outside the separation zone, velocities exceed the free-stream reference and turbulent fluctuations are negligible, whereas inside the wake, velocities are attenuated by up to 70% and fluctuations become comparable to the mean flow. The CFD maps were then transformed and interpolated into recorded tug-assisted maneuvering simulations of bulk carriers with comparable hull forms. The corrected currents at tugboat positions differed non-negligibly from the undisturbed currents originally employed, with average absolute differences of 0.18 m/s and 0.14 m/s in two campaigns, alongside substantial changes in tug drift angle. Recalculation of tug operational parameters showed that, while load differences remained within ±3% for most observations, tail deficits of up to −24% occurred at specific instants. The results demonstrate that ship-induced flow distortion can materially affect the hydrodynamic conditions experienced by tugboats and should be considered in future developments of ship maneuvering simulators. The complete CFD database and an interpolation routine are made publicly available to support reproducibility and future validation.]]></description>
      <pubDate>Thu, 28 May 2026 16:16:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705021</guid>
    </item>
    <item>
      <title>Quadratic Programming-Based Autonomous Cruise Control of an Intelligent Tugboat</title>
      <link>https://trid.trb.org/View/2694329</link>
      <description><![CDATA[To address the multi-objective control problem of autonomous cruising, collision avoidance, and input constraints in intelligent tugboats, a quadratic programming-based autonomous cruise control method is proposed. The method enables the tugboat to reach the target location with prescribed speed, heading, and path while rigorously avoiding collisions within its actuation limits. First, a desired control input is derived using back-stepping and sliding mode control to ensure asymptotic stability of the tracking error. Second, based on Nagumo's theorem, the positional constraints for safe collision avoidance of the tugboat are equivalently transformed into input constraints, effectively preventing any collisions with other vessels. Third, a unified controller is synthesized using a quadratic programming approach to optimally balance cruising control, collision avoidance, and input limitations. Finally, simulation results demonstrate that the proposed quadratic programming-based autonomous cruise control method enforces the prescribed safety distance and actuator limits, while avoiding large or persistent deviations from the reference trajectory and allowing the desired speed and heading to be re-established rapidly after the avoidance maneuver.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694329</guid>
    </item>
    <item>
      <title>A novel mathematical modeling and simulation of multi-tugboats assisted pushing operations for a barge</title>
      <link>https://trid.trb.org/View/2695185</link>
      <description><![CDATA[This paper proposes a novel mathematical model for the simulation of multi-tugboats assisted pushing operations for a barge, addressing the common simplification in existing studies where tugboats are treated as ideal thrusters, neglecting the influence of the hydrodynamic and environmental disturbances of the tugboats on the pushing force. By assuming fixed relative positions between the tugboats and the barge, the model incorporates the hydrodynamic characteristics and environmental disturbances of the tugboats, using the actual thrust of main thrusters as the control input instead of an idealized pushing force. The proposed low-frequency model integrate mass, damping, and disturbance terms derived from each tugboat, enhancing physical realism without significantly increasing computational complexity. Simulation results with various environmental disturbances demonstrate that, while barge positioning performance remains comparable between the conventional and proposed models, the required main thruster thrust substantially differs from the idealized pushing force due to tugboat self-disturbance compensation. The study further reveals frequent thruster saturation phenomena, indicating that the conventional model should apply a conservative loss rate to pushing force change rates to ensure feasibility. The proposed approach provides a more accurate foundation for controller design and performance evaluation in pushing operations.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2695185</guid>
    </item>
    <item>
      <title>Integrated routing optimization of pilotage and tugging services</title>
      <link>https://trid.trb.org/View/2656086</link>
      <description><![CDATA[Seaports are important connections between inland and maritime transportation. During the vessels’ entering/leaving ports, the pilotage service is necessary to mitigate risk, especially for large vessels and congested ports. In the pilotage process, pilots are transported by pilot boats to board the vessels and provide guidance until the vessels’ arriving/leaving the berths, and tugboats are in charge of providing horsepower for vessels to move safely near the port. In this paper, a joint optimization problem considering the pilotage and tugging services is studied. Realistic constraints, including the multi-waypoints of tugboats, required service time windows of vessels, different types of tugboats and pilots. A mixed integer programming model is introduced, and small-size instances are solved by CPLEX solver. To solve large-scale instances, an adaptive large neighborhood search algorithm with linear programming models (ALNS-LP) together with a tailored feasibility check procedure and cost evaluation process is proposed. Extensive computational experiments are conducted to verify efficiency and effectiveness of the algorithm and obtain some managerial insights for port operators.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656086</guid>
    </item>
    <item>
      <title>One Consideration on Achievement of a Head-out Mooring - A Method to Decrease the Yaw Moment during A Turning Short Around Assisted by the Tugs</title>
      <link>https://trid.trb.org/View/2669601</link>
      <description><![CDATA[As a countermeasure of a quick evacuation from ports and harbors for large ships against a great earthquake and tsunami, it is important to achieve a head-out mooring, instead of a head-in mooring ordinarily conducted in larger ships. It is also a key issue to obtain a property of yaw moment acting on the ship in shallow water during a turning short around assisted by tugs. From several pure yaw rotating tests in a water tank for a model ship conducted in this study, it is found that the yaw moment in shallow water where the ratio(h/d) of water depth(h) and draft(d) is h/d=1.2, is decreased by digging down the sea bottom of the turning basin only until the ratio of h/d=2.0, as well as that in the ratio of h/d ≧ 2.0.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:23:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669601</guid>
    </item>
    <item>
      <title>Scenario generation for testing autonomous escort operations of intelligent tugs based on surrogate modeling and hybrid sampling</title>
      <link>https://trid.trb.org/View/2653200</link>
      <description><![CDATA[Tugs play an important role in port operations by assisting in the safe guidance and berthing of large ships. With the development of autonomous functions for tugs, testing and evaluation are indispensable to ensure their development and deployment. The targeted identification and generation of critical test scenarios characterized by high challenge and risk is necessary for improving testing efficiency and effectiveness. This paper proposes a scenario generation method for testing the autonomous escort operations of intelligent tugs based on surrogate modeling and a hybrid sampling strategy. The surrogate model is trained on extensive full-scale simulations using a marine simulator to efficiently approximate the relationship between scenario parameters and challenge indicators. The hybrid sampling approach integrates adaptive sampling, which focuses on generating base scenarios involving tug and target ship interactions, with an improved arithmetic optimization algorithm (AOA)-based sampling to produce encounter scenarios that capture collision risks with neighboring obstacle ships. Experimental results demonstrate that the proposed method can efficiently generate a diverse set of challenging and high-risk scenarios across varying complexity levels, significantly outperforming traditional random sampling methods. These scenarios facilitate systematic validation and performance evaluation of autonomous escort functions in realistic maritime environments.]]></description>
      <pubDate>Mon, 06 Apr 2026 08:50:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2653200</guid>
    </item>
    <item>
      <title>Research on multi condition electric load forecasting of tugboat based on graph structure temporal parallel fusion framework</title>
      <link>https://trid.trb.org/View/2679971</link>
      <description><![CDATA[Ship power load forecasting is a crucial aspect of energy management and green shipping, significantly contributing to the optimization of fuel consumption, reduction of emissions, and enhancement of operational safety. However, the operating environment for tugboats is complex and variable, with load signals influenced by multiple factors, such as working condition transitions, current disturbances, and meteorological conditions. These factors result in strong nonlinearity and time-varying characteristics. To address this challenge, this paper proposes a Graph Structure-Temporal Parallel Fusion Prediction Framework (GTPF). This framework enables dual-path parallel modeling: it employs the BiLSTM-Attention model to capture the temporal evolution of the load, while concurrently constructing a sample-level dynamic graph that incorporates knowledge graph priors, combined with a Graph Convolutional Network (GCN) to extract the structural dependencies among multiple variables. Ultimately, through a gating mechanism, the structural and temporal features are adaptively fused, ensuring high-precision predictions under complex working conditions. The results indicate that the proposed method outperforms traditional models in multi-condition power load forecasting for tugboats, achieving determination coefficients (R²) of 0.9809, 0.9093, and 0.9691 under three typical working conditions, respectively. This holds significant application value for ship energy efficiency management and intelligent scheduling.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679971</guid>
    </item>
    <item>
      <title>Experimental investigation on deep and shallow water resistance and seakeeping of articulated Tug Barge</title>
      <link>https://trid.trb.org/View/2682787</link>
      <description><![CDATA[The resistance characteristics and seakeeping performance of Articulated Tug Barges (ATBs) differ markedly from those of conventional vessels, especially during coupling and decoupling operations. This study focuses on a twin-pin ATB designed for coastal and inland waterways, where shallow water effects and wave encounters are prevalent in operational routes. Model tests assessed calm-water resistance in deep and shallow conditions for the tug, barge, and integrated ATB system at design draft with water depth to draft ratios of h/T = ∞, 2.0, 1.5, 1.2, and ballast draft of h/T = ∞, 6.25, 4.69, 3.75. Seakeeping evaluations in regular head waves across various speeds and wavelength-to-length ratios λ/Lpp = 0.3∼3.8 measured wave-added resistance, heave, pitch, and vertical accelerations at bow/mid/stern sections. Key findings include: 1) Shallow-water resistance and its increments increase substantially as h/T decreases. 2) Shallow-water resistance characteristics of the ATB, barge, and tug exhibit distinct trends with the depth Froude number Frh. 3) The tug shows larger non-dimensionalized peak amplitudes in wave-added resistance, heave, and pitch compared to conventional vessels, with peak responses occurring at λ/Lpp ≈ 2.0. 4) The barge's motions resemble those of traditional vessels, whereas the tug's responses are significantly influenced by the barge.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682787</guid>
    </item>
    <item>
      <title>Integrated optimization of vessel sequencing and tugboat scheduling under extreme weather</title>
      <link>https://trid.trb.org/View/2644028</link>
      <description><![CDATA[In recent years, increasingly frequent extreme weather has posed severe challenges to designing vessel entry and exit sequences and tugboat schedules at ports. This study focuses on the integrated optimization of vessel sequencing and tugboat scheduling under extreme weather, emphasizing their coupling and the cascading effects that such conditions may trigger. The study proposes a weather-driven rolling decision mechanism that assesses the interference severity based on specific extreme weather conditions to determine the selected adjustment strategy (interference management or rescheduling strategy). On this basis, an integer programming model is formulated to minimize total vessel in-port time and total tugboat operation time, while incorporating tugboat-vessel coupling and resource constraints on the channel and tugboat under extreme weather. To solve the model efficiently, a hybrid algorithm combining a Genetic Algorithm and an Adaptive Large Neighborhood Search (GAALNS) is developed to generate integrated rescheduling plans. Finally, this study validates the approach through numerical experiments, demonstrates algorithmic superiority, and conducts three sensitivity analyses to examine the effects of key factors on the objective function value. The results provide practical guidance for port authorities in formulating timely vessel sequences and tugboat schedules after extreme weather.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2644028</guid>
    </item>
    <item>
      <title>Energy consumption prediction for electric tugboats using GA-BiLSTM and adaptive operational mode recognition</title>
      <link>https://trid.trb.org/View/2667990</link>
      <description><![CDATA[Accurate prediction of energy consumption is vital for optimizing the efficiency of electric vessels, alleviating range anxiety, and ensuring safe navigation. However, the frequent switching of operational modes in electric tugboats leads to highly fluctuating energy consumption patterns that are difficult for general-purpose models to capture. This paper proposes a operational-adaptive energy consumption prediction method for electric tugboats based on a genetic algorithm-optimized bidirectional long short-term memory (GA-BiLSTM) network. First, the temporal characteristics of real navigation data are analyzed. Energy consumption per unit distance is employed as a workload intensity factor to distinguish operational modes such as sailing, towing, and pushing, thereby constructing a dataset with operational features. Second, a baseline prediction model is established using the BiLSTM network to capture bidirectional temporal dependencies. The key hyperparameters including network depth, neuron count, and learning rate, are subsequently optimized via a genetic algorithm, resulting in the GA-BiLSTM prediction model. Experimental results indicate that the proposed model outperforms approaches such as Extreme Trees (ET), XGBoost, and Random Forest (RF), achieving a mean squared error (MSE) as low as 0.0897. These findings highlight the potential to enhance endurance and improve the operational efficiency of electric tugboats.]]></description>
      <pubDate>Tue, 24 Feb 2026 15:39:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667990</guid>
    </item>
    <item>
      <title>Intelligent Multi-Objective Tugboat–Barge Scheduling for Inland Waterway Operations Using Generative Adversarial Learning and Reinforcement-Based Optimization</title>
      <link>https://trid.trb.org/View/2658726</link>
      <description><![CDATA[Tugboat–barge coordination in inland waterway transportation presents critical multi-objective optimization challenges due to interdependent constraints including fleet capacity, operational costs, dynamic tidal conditions, and temporal accessibility windows. Traditional approaches fail to effectively address these complex interdependencies in constrained inland waterway environments. This paper proposes Multi-Objective Generative Adversarial Learning and Search for Intelligent Transportation Systems (MGALS-ITS), integrating reinforcement learning-based construction, generative adversarial network-driven local search, and adaptive optimization specifically for tugboat–barge scheduling in tidal inland waterways. The Reinforcement Learning (RL) component learns from constraint patterns to generate feasible, cost-efficient coordination schedules for tugboat–barge operations. A conditional Wasserstein Generative Adversarial Network (GAN) refines solutions through learned neighborhood exploration, while adaptive strategies and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) enable real-time cost-makespan trade-offs. Experimental validation on comprehensive inland waterway scenarios involving 103 tugboats, 80 barges, and 48 customer destinations demonstrates superior performance over conventional scheduling methods. MGALS-ITS achieves lowest operational costs and shortest completion times, surpassing Long Short-Term Memory and Random Forest (LSTM+RF) baselines while generating 15.4% more diverse solutions and 31% more feasible configurations than existing systems, with 20–35% greater resilience against operational disruptions. This research positions MGALS-ITS as an adaptive decision support framework for tugboat–barge operations in inland waterway networks, offering significant performance improvements for tidal waterway logistics optimization.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658726</guid>
    </item>
    <item>
      <title>Optimization of load reduction and berth shifting operations in green ports: a collaborative scheduling model for berths, unloaders, and tugboats</title>
      <link>https://trid.trb.org/View/2648152</link>
      <description><![CDATA[With the increasing trend of large-scale vessels, the shortage of deep-water berth resources in ports has become increasingly prominent, severely restricting vessel turnover rates and cargo unloading efficiency. To address this bottleneck, this study proposes an optimization approach for load reduction and berth shifting of large dry bulk carriers, coordinating the allocation of core resources such as berths, unloaders, and tugboats to enhance the overall operational efficiency of ports. A multi-objective, two-stage joint scheduling optimization model for berth, unloader, and tugboat operations is developed. The model decouples the problem into two stages: berth-unloader joint allocation and tugboat scheduling. It comprehensively considers operation time and costs to achieve full-process optimization of load reduction and berth shifting. Furthermore, a two-layer solution framework integrating the Starfish Optimization Algorithm (SFOA) with the Non-dominated Sorting Genetic Algorithm (NSGA-II) is proposed. The framework employs tabu search to enhance Pareto front exploration in berth-unloader allocation and utilizes an elite-based chromosome generation mechanism in tugboat scheduling to improve solution quality. Additionally, a multi-energy hybrid tugboat fleet comprising diesel and clean energy-powered vessels is designed, along with a vessel-tugboat matching strategy that factors in emission reduction requirements based on varying tugboat demands of different bulk carrier sizes. Finally, a case study based on a real port in northern China demonstrates the effectiveness of the proposed optimization scheme in alleviating deep-water berth shortages, reducing tugboat emissions, and promoting the intelligent and green transformation of port operations.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:32:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648152</guid>
    </item>
    <item>
      <title>Towards the development of an operational fail-safe approach for autonomous tugboats</title>
      <link>https://trid.trb.org/View/2582937</link>
      <description><![CDATA[Ensuring adequate operational safety for future marine systems with high levels of automation and low levels of human involvement will require the development of approaches that facilitate failing with safety, as highlighted by guidelines by Classification Societies. Designing fail-safe behaviour for autonomous marine systems is currently based on different types of hazard analyses that mostly depend on expert opinion about how to achieve this in different scenarios. The objective of this paper is to present a methodology for determining the decision paths that lead to suitable and feasible Minimum Risk Conditions (MRCs) outside the operational envelope given specific triggering events. The methodology integrates system modelling techniques with the Systems Theoretic Process Analysis (STPA) and is demonstrated in a case study involving two autonomous tugboats that manoeuvre a vessel to its berth while being monitored by a remote operator. The analysis resulted in three main and one last resort MRC that were associated to different scenarios based on the available system functionalities. The authors' aim is to contribute towards determining design requirements related to the behaviour of autonomous marine systems outside their operational envelope, as well as developing formal approaches for verifying a minimum level of safety in abnormal conditions.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582937</guid>
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