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    <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" />
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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>Metaheuristic approaches for the Manhattan metric straddle carrier routing problem with buffer areas</title>
      <link>https://trid.trb.org/View/2704178</link>
      <description><![CDATA[This study investigates an optimization problem in container terminals, where straddle carriers (SCs) transport containers between seaside and stacking areas. Container transportation sequences respect both the predetermined loading sequence at quay cranes (QCs) and the capacity restrictions of buffer areas located below QCs for container exchange between SCs and QCs. We propose two sets of strategies. The first prioritizes runtime efficiency, employing methods such as a local search procedure and two variants of Variable Neighborhood Descent. For the second strategy, we use two different metaheuristics, namely Variable Neighborhood Search and a Greedy Randomized Adaptive Search Procedure, to provide solutions with superior objective function values. The performance of these methods is assessed in an extensive computational study and compared with benchmarks from the literature, showing that the proposed methods effectively allocate containers to SCs, resulting in minimized idle times of QCs and, consequently, shorter turnaround times for vessels.]]></description>
      <pubDate>Wed, 02 Sep 2026 09:21:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704178</guid>
    </item>
    <item>
      <title>PSO-Optimized LQR Control for Anti-Sway and Positioning of Rotary Cranes</title>
      <link>https://trid.trb.org/View/2742660</link>
      <description><![CDATA[This paper proposes a Linear Quadratic Regulator (LQR) parameter optimization method based on Particle Swarm Optimization (PSO) to enhance the grab attitude controller for rotary crane systems, with the objectives of improving positioning accuracy and suppressing load swing. Lagrange’s equations are first used to create a nonlinear dynamic model of the rotary crane, which is then linearized around an operational point to produce a fourth-order state-space representation. Based on this representation, a dual-objective fitness function is created, employing the Integral of Time multiplied by Absolute Error (ITAE) as the performance index and assigning the swing angle error more weight. The important parameters of the LQR weight matrix are optimized using the PSO algorithm. A dedicated novel pre-compensation gain algorithm is then developed to solve the pseudo-inverse of an augmented matrix, thereby removing steady-state error. According to simulation results, the PSO-optimized controller greatly improves the anti-sway performance and positioning accuracy of the system by reducing the peak swing angle and settling time by 33.9% and 55.9%, respectively, as compared to the traditional LQR control.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742660</guid>
    </item>
    <item>
      <title>Finite Element Analysis of Key Structures in Spider Cranes</title>
      <link>https://trid.trb.org/View/2742641</link>
      <description><![CDATA[In order to solve the problem of poor terrain adaptability of traditional cranes in the construction of transmission lines in mountainous areas, and to ensure the safe operation of light modular spider cranes in complex terrain, this study is modelled on spider cranes with a rated lifting capacity of 3 tons. According to the Crane Design Specification and the Crane Design Manual, the static finite element analysis of the core structure was carried out using UG and ANSYS Workbench software. Following the principle of balancing load-bearing accuracy and calculation efficiency, the upper and lower structures of the spider crane are simplified in layers. Subsequently, the reaction force, displacement and stress characteristics of the core structure were analysed under the condition of a rated load of 3 tons and the minimum working radius. The research results show that the strength and rigidity of the core structure of the spider crane meet the standard requirements to ensure that it can operate safely in mountainous environments.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742641</guid>
    </item>
    <item>
      <title>Two-Level Crane Scheduling with Hybrid Metaheuristics in Realistic Port Operations</title>
      <link>https://trid.trb.org/View/2742728</link>
      <description><![CDATA[Sustainable and efficient container unloading is a critical operation in maritime logistics, as it directly affects port throughput and ship turnaround time. Since quay cranes are the primary equipment responsible for this task, their effective scheduling is essential to minimize delays and ensure operational stability. This paper presents a two-level optimization approach for quay crane scheduling that balances operational efficiency with ship stability. At the bay level, we compare a flexible crane assignment strategy with the port’s current strategy, which ensures ship stability but results in a long total unloading time. Using the LINGO solver and a Genetic Algorithm (GA), we achieve up to a 15% improvement. To leverage the port’s strategy while overcoming its inefficiency, we redefine the unloading unit at the tier level and structure the process into two sequential phases. A hybrid metaheuristic combining the Firefly Algorithm (FA) and Cuckoo Search (CS) is proposed and compared with the GA. The hybrid algorithm shows higher consistency and statistically comparable solution quality to the GA.]]></description>
      <pubDate>Mon, 10 Aug 2026 15:03:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742728</guid>
    </item>
    <item>
      <title>A heuristic algorithm based on quantum behaviour for berth allocation and quay crane scheduling optimisation problems in container terminals</title>
      <link>https://trid.trb.org/View/2698269</link>
      <description><![CDATA[Various real-world engineering examples need to be settled by appropriate methods. However, many of them are proved as NP-hard problems with huge computational complexity, which causes premature convergence and slow computing efficiency. In this study, we are concerned with the combinatorial optimisation problems for optimal scheduling tasks in container terminals by utilising and improving the quantum-behaviour heuristic algorithm. First, an integrated two-stage model for the berth allocation, quay crane assignment, and quay crane scheduling problem (BACASP) in container terminals is presented to minimise the running costs in the given time horizon. To deal with the computation demand, a quantum-behaviour heuristic algorithm (QGA-E) with stronger global searching ability and higher computation efficiency is developed. The above works are certified to be feasible according to a series of experimental studies with datasets from the real container terminal.]]></description>
      <pubDate>Wed, 29 Jul 2026 09:16:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698269</guid>
    </item>
    <item>
      <title>Dynamic modeling and applicability analysis of a modular rope-driven anti-sway system for A-frames using adaptive fuzzy PID control</title>
      <link>https://trid.trb.org/View/2725531</link>
      <description><![CDATA[To address the problem that the payload of an A-frame is prone to sway under environmental disturbances during offshore lifting operations, this paper proposes a Modular Rope-Driven Anti-Sway System (MRAS) applicable to A-frames. It can be rapidly deployed at designated positions on the deck without permanent modification to the original crane structure, and spatial constraints are imposed on the payload through multiple anti-sway ropes, thereby improving the stability of the lifting process. First, a system dynamic model is established by considering the coupled effects of vessel motion, wind loads, wave excitation, the main hoisting rope, the anti-sway ropes, and payload motion. Based on this model, the variation characteristics of the anti-sway rope length and velocity under different main hoisting rope lengths and A-frame slewing angles are analyzed. The results show that external excitation has a relatively limited influence on the required anti-sway rope length, but significantly increases the demand for the velocity response of the anti-sway ropes. Subsequently, based on the established dynamic model, numerical simulations are carried out under typical operating conditions, to analyze the anti-sway performance of the MRAS. Furthermore, the applicability of the system is evaluated under unsteady disturbance conditions such as abrupt changes in wave height, wave direction, and wind load, and a preliminary mechanical check of the permanent magnetic fixing scheme is also performed. The simulation results indicate that, after the MRAS is implemented, both the in-plane and out-of-plane sway angles of the payload are significantly suppressed, and the system response becomes smoother. Under abrupt disturbances, the MRAS can still effectively reduce the peak sway angle and improve the process by which the system recovers stability. Finally, experimental verification is conducted under several typical operating conditions using a scaled experimental platform. The results show that the MRAS can effectively reduce the sway amplitude of the payload and provide auxiliary stabilization of the payload attitude. Overall, the proposed MRAS demonstrates favorable anti-sway performance, deployment flexibility, and engineering applicability, and can provide a reference for improving the stability of offshore lifting operations using A-frames and for the design of anti-sway devices.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725531</guid>
    </item>
    <item>
      <title>Resilience assessment of multimodal container ports under operational disruptions: a global sensitivity analysis</title>
      <link>https://trid.trb.org/View/2688701</link>
      <description><![CDATA[Assessments of port operational resilience are often fragmented in the existing literature and practices, primarily concentrating on local-level risks related to individual components and/or subsystems. These evaluations treat each component and subsystem in a port independently, neglecting the interconnected ripple effects throughout the port from a multimodal perspective. Consequently, improvements targeting individual components may not yield an optimal outcome for the entire port system. Key components, including liner shipping, feeder shipping, railroads, and trucking, constitute the fundamental operational structure of a multimodal container port. As ports evolve to incorporate new technologies, the complexity of their operations increases, emphasising the need for accurate management of port operational resilience. In response, this paper introduces a novel methodology to assess multimodal port resilience by quantifying the impact of various disruptions and identifying the interactions among them that could lead to a ripple effect. In this framework, port operations are first simulated through System Dynamics (SD) modelling. The resulting performance is then transformed into resilience indicators, which are synthesised across subsystems using the Evidential Reasoning (ER) method. Finally, the Sobol Global Sensitivity Analysis (GSA) is employed to assess the influence of individual and joint disruptions. To assess the consistency of the results, the Intraclass Consistency Coefficient (ICC) is calculated for three different GSAs. Using historical failure data and field evidence, multiple disruption scenarios are explored. The outcomes indicate that failures of yard and quay cranes have the greatest impact on port resilience. It is further observed that many disruptions arise from interdependent failures rather than individual malfunctions, leading to amplified ripple effects. The results provide a data-driven foundation for policymakers and port managers to shift from experience-based to evidence-based allocation of emergency resources, with evidence generated through large-scale simulations of plausible but previously unobserved disruption scenarios. They also support cross-agency coordination for integrated resilience management against compound disruptions arising from ripple effects. In summary, this framework, for the first time, offers crucial insights for bolstering long-term resilience in multimodal container port operations from a systematic overall perspective.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688701</guid>
    </item>
    <item>
      <title>A multi-objective approach for the integrated allocation of quay cranes, internal trucks, and yard cranes in container terminal considering actual handling efficiency</title>
      <link>https://trid.trb.org/View/2684676</link>
      <description><![CDATA[The growth of maritime container trade volume and the frequent berthing of ships require container terminals to shorten the ship turnaround time. As the main production tool of container terminal, the reasonable allocation of equipment resources is conducive to minimizing the ship turnaround time and the operating cost of container terminal. In order to provide a reasonable and refined equipment configuration scheme, this paper studies the joint allocation of quay cranes (QCs), internal trucks (ITs), and yard cranes (YCs) in container terminal with consideration of container batch operations. Two formulas are proposed to measure the relationship between the number of IT, QC work efficiency, and YC work efficiency. A multi-objective mixed integer programming model for the joint allocation of QCs, ITs, and YCs is established to minimize the ship turnaround time, the operating cost of the terminal side, and the operating cost of the yard side. Among them, three configuration profile decision variables are proposed to describe the equipment allocation scheme. To solve this problem, an enhanced multi-objective evolutionary algorithm based on the fitness evaluation mechanism of fuzzy correlation entropy is designed, and an adaptive local reinforcement search strategy founded on tabu search and multiple neighborhood structures is introduced to improve the global search capability. Finally, the Shanghai Port is taken as a case study, and experiments of different scales verify the effectiveness of the model and method.]]></description>
      <pubDate>Wed, 01 Jul 2026 09:36:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684676</guid>
    </item>
    <item>
      <title>A Self-Adaptive Monte Carlo Tree Search Algorithm for Generalized Quay Crane Scheduling Problem</title>
      <link>https://trid.trb.org/View/2686235</link>
      <description><![CDATA[Efficient quay crane (QC) handling is crucial for enhancing service levels and competitiveness in container terminals, particularly with the advent of ultra-large vessels necessitating rapid container turnover. As the complexity of terminal operations escalates, this paper addresses the generalized quay crane scheduling problem (GQCSP), aiming to minimize the makespan for discharging and loading operations. In this problem, both 20-ft and 40-ft containers are mixed and stacked above and below the hatch covers, and QCs operate multidirectionally under safety distance and noncrossing constraints. A solution approach is proposed that delivers swift, feasible solutions over protracted optimal ones, which is essential for adapting to last-minute operational changes. The problem is formulated as a Markov decision process model, and a self-adaptive Monte Carlo tree search (MCTS) algorithm is proposed that can handle up to 11 QCs and 280 container groups. For algorithm acceleration, problem-specific methods are developed, achieving an improvement of approximately 1.2% of the makespan and a reduction of two-thirds of the computation time. Specifically, an adaptive lower bound is introduced for node selection and subtree pruning, accompanied by a lower-bound-based reward function to facilitate backpropagation. The experiments demonstrate that the proposed MCTS algorithm significantly outperforms existing heuristic algorithms, achieving average improvements of 20.84%, 14.40%, and 11.02% in terms of makespan compared with the strategy-based approach, while also surpassing the dynamic programming algorithm by 12.88% and the memetic algorithm by 72.79%. Furthermore, compared with the most recent Benders decomposition method assuming a homogeneous container size, the proposed MCTS algorithm also demonstrates comparable performance where the difference is less than 0.4%. The significance of the results demonstrates that the proposed approach has great universality, thus improving terminal productivity and responsiveness.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686235</guid>
    </item>
    <item>
      <title>AUTOMODAL - Automation of Inland Container Terminal Cranes Solving People Detection in the Operating Range</title>
      <link>https://trid.trb.org/View/2671804</link>
      <description><![CDATA[The AutoModal project results show the prototypical end-to-end automation of the transshipment terminal. An essential component is the automation of a gantry crane, which was converted for this purpose so that independent automated processes could be carried out. The focus of the work was on the conversion and prototypical operation of the crane automation in a reference terminal. For this purpose, the gantry crane was equipped with additional sensors to ensure safe and reliable detection of persons in the crane environment and to enable automated operation. Suitable solutions were evaluated from a wide selection of sensors and tested on a test model. In parallel, suitable control software was developed, interfaces harmonized and hardware components integrated for operation. The software modules developed was published onto the GitLab Open Source platform. The following article describes the main outcome of the project. AutoModal was carried out as part of the funding programme for innovative port technologies of the Federal Ministry for Digital and Transport Affairs.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671804</guid>
    </item>
    <item>
      <title>Marine Investigation Report: Collapse of Emissions Control Barge STAX 1 Capture and Control Articulated Arm, June 2, 2024</title>
      <link>https://trid.trb.org/View/2714450</link>
      <description><![CDATA[​On June 2, 2024, about 1649 local time, the emissions control barge STAX 1 was capturing emissions from the containership Erving at the Fenix Marine Services Container Terminal in the Port of Los Angeles, Los Angeles, California, when a ship-to-shore container crane struck the barge’s capture and control articulated arm, causing it to collapse, and sections of it fell onto the barge, onto the Erving, and into the water. The arm’s hydraulic system released about 10 gallons of hydraulic oil onto the deck of the Erving and into the water. One person on board the STAX 1 received minor injuries. Damages were estimated at $3.2 million. The National Transportation Safety Board (NTSB) determined that the probable cause of the collapse of the emissions control barge STAX 1 capture and control articulated arm was a shoreside crane operator moving a ship-to-shore container crane without verifying the crane’s lowered boom had ample clearance over any obstructions, and the container terminal’s inadequate guidance for gantrying cranes.​​]]></description>
      <pubDate>Mon, 29 Jun 2026 09:11:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714450</guid>
    </item>
    <item>
      <title>Numerical and experimental investigations on the coupled response of DP crane vessel-topsides in waves</title>
      <link>https://trid.trb.org/View/2673237</link>
      <description><![CDATA[The coupled motion responses between the lifting topsides and the crane vessel are quite complex in the offshore platform installation. To ensure the safety of lifting operations, achieving precise positioning of the crane vessel and restraining the motions of the lifting topsides is still a challenging task. In this study, the offshore topsides lifted by a dynamic positioning (DP) crane vessel are experimentally and numerically modeled. In the numerical model, a time-domain model of the vessel-topsides system based on the coupled stiffness matrix method is established. For the DP system, a fuzzy Proportional-Integral-Differential (PID)/genetic algorithm-based DP control system is developed in both the numerical and experimental models. The fuzzy control theory is applied to improve the control ability of the PID controller. An improved genetic algorithm is used to solve the thrust distribution problem. In the model tests, vessel and topsides motions, as well as the thrust of the DP system, are experimentally measured. The coupled motions of the vessel-topsides system and the DP system performance in different environmental conditions are evaluated. Good agreements between numerical and experimental results are achieved, demonstrating the feasibility of the coupled motion model and the robustness of the DP system. The experimental and numerical motion results are further statistically analyzed to evaluate the DP capability and lifting safety based on the guidelines. The proposed approach can serve as an effective and accurate way to simulate the coupled motions in the topsides lifting process for practical engineering.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673237</guid>
    </item>
    <item>
      <title>Understanding and predicting quay crane breakdowns using explainable AI</title>
      <link>https://trid.trb.org/View/2701435</link>
      <description><![CDATA[Quay cranes (QCs) play a vital role in ship-to-shore operations, enabling the seamless transfer of cargo between sea and land. However, increasing trade volumes require faster and more cost-effective container handling, exerting significant pressure on QCs and leading to greater wear on critical components such as wires, hoists, and rope clamps. While operations research has explored maintenance scheduling to improve terminal performance, comparatively little work has examined how machine learning can exploit the growing volume of QC monitoring and operational data to predict breakdowns before they occur. This study contributes to this area by integrating terminal operations data, QC monitoring logs, and meteorological observations into a unified analytical framework. We employ explainable artificial intelligence (XAI), using both global and local SHapley Additive exPlanations (SHAP) to identify the operational and environmental factors most strongly associated with QC failures and to illustrate concrete, instance-level examples of how specific conditions contribute towards breakdowns. In parallel, we develop a robust machine learning pipeline built around nested cross-validation to assess the predictive capability of multiple classifiers for forecasting QC breakdowns. Our XAI analysis reveals that breakdown risk is closely linked to QC working time, the distribution of moves across simultaneously operating QCs, hoist overload and trolley alignment warnings, and adverse weather conditions. Among the evaluated models, LightGBM achieved the highest predictive accuracy, reaching up to 83% in identifying breakdown-prone scenarios. These findings demonstrate the feasibility and value of data-driven predictive maintenance for QCs, providing insights that support safer, more reliable, and more efficient terminal operations.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701435</guid>
    </item>
    <item>
      <title>Integrated Container Slot Allocation and Automated Stacking Crane Scheduling in Automated Container Terminals with Limited Buffers</title>
      <link>https://trid.trb.org/View/2703736</link>
      <description><![CDATA[This paper investigates the integrated scheduling of automated stacking cranes (ASCs) and container slot allocation in automated container terminals (ACTs) with limited buffer capacity. A hybrid stacking strategy based on time windows is proposed, and a bi-objective mixed-integer programming model is developed, considering automated guided vehicles and truck buffer capacities, ASC safety distances, and handshake area operations. An enhanced non-dominated sorting genetic algorithm II with tabu search (NSGA-II-TS) is designed, with parameters optimized via sensitivity analysis. Experiment results show that comparisons with an exact mixed-integer linear programming solver validate the solution quality of the proposed approach, and that the proposed algorithm significantly outperforms benchmark heuristic methods in generating high-quality Pareto-optimal solutions. Case studies reveal that dynamically adjusting handshake area locations and setting buffer capacity to six units effectively balance container flow and operational costs. The proposed approach is also validated against two alternative scheduling strategies, demonstrating superior effectiveness. This research provides new strategies and a robust method for improving the operational efficiency of ACTs under buffer constraints.]]></description>
      <pubDate>Sat, 16 May 2026 12:15:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703736</guid>
    </item>
    <item>
      <title>Marine Investigation Report: Crane Wire Failure on Offshore Construction Vessel Island Venture and Subsequent Damage to Offshore Supply Vessel C-Enforcer, April 3, 2025</title>
      <link>https://trid.trb.org/View/2696166</link>
      <description><![CDATA[On April 3, 2025, about 2145 local time, the offshore construction vessel Island Venture’s crane was being used to lift a wire reel from the back deck of the offshore supply vessel C Enforcer in Bayou Lafourche, Port Fourchon, Louisiana, when the crane’s hoisting wire parted, causing the reel to drop onto the C Enforcer’s main deck. There were no injuries, and no pollution was reported. Damage to the C Enforcer and Island Venture was estimated to be $3.8 million. The National Transportation Safety Board (NTSB) determined that the probable cause of the failure of the hoisting wire on the offshore construction vessel Island Venture’s crane was internal corrosion of the crane’s hoisting wire.​]]></description>
      <pubDate>Tue, 05 May 2026 10:18:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696166</guid>
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