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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>Container Terminal Resource and Performance Comparison of Self-Propelled Autonomous Railcars and Conventional Intermodal Trains</title>
      <link>https://trid.trb.org/View/2772602</link>
      <description><![CDATA[As demand for intermodal freight transportation in the United States continues to grow, it is essential to develop strategies that enhance the capacity and operational efficiency of transporting containers by rail. Self-Propelled Autonomous Railcars (SPARCs) are one promising technology to enhance rail/road intermodal rail service, and this study quantifies their potential terminal area, resource, performance, and energy benefits in comparison to conventional trains (CTs). An integrated analysis framework was developed that combines high-level infrastructure layout optimization with simulation of operational-level terminal congestion to evaluate terminal performance in terms of container processing time and energy consumption. Various output metrics were compared for SPARCs and CTs across different train/vehicle configurations and daily throughput volumes. The results indicated that SPARCs consistently outperformed CTs in both spatial and temporal efficiency. From a strategic planning perspective, typical CT lengths required nearly three times the yard footprint of the shortest SPARC platoons to process the same number of containers. From an operational perspective, SPARCs achieved shorter processing times for both inbound and outbound containers, experienced lower train delays, and required fewer resources for equivalent throughput. Delay function coefficients fit to power functions indicated that CTs were more sensitive to train frequency, leading to earlier system saturation under limited resources. Furthermore, to achieve comparable processing times, CTs required significantly more terminal hostlers than SPARCs. Based on these results, SPARCs offer greater flexibility and energy efficiency than CTs for low-volume short-haul corridors, making them a promising alternative to expand the network reach of future intermodal freight rail systems.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772602</guid>
    </item>
    <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>
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    <item>
      <title>Reconceptualising Container Port Performance: Evaluating Efficiency and Strategic Importance of Ports within Shipping Network Configurations</title>
      <link>https://trid.trb.org/View/2743008</link>
      <description><![CDATA[Widely used commercial indices for assessing port performance such as the Container Port Performance Index (CPPI) and the Liner Shipping Connectivity Index (LSCI), primarily focus on one particular dimension — either emphasising in-port operational efficiency or port connectivity — and may overlook the interdependencies that exist between port operations and the broader network configurations that they are part of. This paper seeks to address this gap by developing an index that integrates both individual port attributes and the relationships between nodes within the shipping network. Using an AIS dataset comprising 5,496 container vessels in 2022, the performance of 88 container ports within the Asia—Europe Container Shipping Network is evaluated. Container port performance is assessed in the context of: port function, port connectivity, and network dependency. The proposed index is further compared with other widely used commercial indices and metrics in the container shipping industry. The results demonstrate that the assessment of port performance can be improved over other indices that concentrate on single perspectives. These findings highlight the necessity of re-evaluating port performance through a network-centric lens, offering deeper insights for stakeholders in port planning, policy, and logistics management.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2743008</guid>
    </item>
    <item>
      <title>A study on dynamic scheduling of AGV in ACTs with consideration of battery constraints</title>
      <link>https://trid.trb.org/View/2709500</link>
      <description><![CDATA[Automated guided vehicles (AGVs) have significantly improved automation and efficiency in automated container terminals (ACTs). As AGVs rely on electric power, their operational range directly influences their overall performance, making effective battery management essential. This study introduces the first application of wireless AGV charging at ports, developing a hybrid AGV charging system. A dual-engine mechanism is proposed: a rule engine formulates task assignment strategies, while a learning engine employing a hybrid metaheuristic-based algorithm optimizes routing. Integrating real-time battery status into scheduling achieves dynamic coordination between task allocation and path planning. Results show wireless charging reduces centralized charging events and shortens AGV idle time by 7.21%. Combined with optimized scheduling, terminal efficiency further improves, reducing operational time by an additional 10.31%. Optimal performance occurs at a 16% charging threshold, balancing charging frequency, equipment lifespan, and scheduling stability. These findings support effective decision-making for AGV energy strategies in ACTs.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709500</guid>
    </item>
    <item>
      <title>Integrated AGV Positioning and Scheduling Using Simulation-Based Reinforcement Learning and Combinatorial Optimization in Automated Container Terminals</title>
      <link>https://trid.trb.org/View/2685898</link>
      <description><![CDATA[Automated Guided Vehicle (AGV) positioning and scheduling critically impact automated container terminal efficiency, yet operational uncertainties significantly challenge effective decision-making. This study addresses the integrated AGV positioning and scheduling problem (AGV-PSP) under uncertainty through a novel framework integrating reinforcement learning (RL), combinatorial optimization, and high-fidelity simulation. We formulate the AGV-PSP as a Markov Decision Process where RL learns the state value function, estimating the long-term operational value of terminal locations. Based on the value function, our positioning policy strategically locates AGVs in high-value areas, while our scheduling policy balances immediate and long-term rewards using integer linear programming solved by an improved Hungarian algorithm. Experiments demonstrate 35.6% higher rewards and 36.6% lower costs compared to benchmarks on average. Sensitivity analyses validate the framework’s robustness across varying uncertainty.]]></description>
      <pubDate>Fri, 28 Aug 2026 16:25:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685898</guid>
    </item>
    <item>
      <title>Mediterranean container port reconfiguration under geopolitical disruption: an AIS-based multi-scale analysis</title>
      <link>https://trid.trb.org/View/2700883</link>
      <description><![CDATA[Recent geopolitical events in the Red Sea have disrupted the passage of commercial vessels transiting through the Suez Canal. In response, shipping lines have rerouted services via the Cape of Good Hope to secure trade between Asia, the Mediterranean and Northern Europe. This paper offers a strategic analysis of the reconfiguration of containerised flows in the Mediterranean, based on Automatic Identification System (AIS) data collected over a 240-day period centred on the onset of the crisis. Rather than seeking to identify the internal decision-making processes of shipping companies, the study examines how network reconfigurations materialised at port level, using a multi-scale analytical approach focused on observable changes in port positioning within the Mediterranean port system. By combining port typology (hubs and gateways), vessel size categories and the reallocation of deployed capacity, this paper analyses relative changes in attractiveness and resilience of the main Mediterranean container ports. The results highlight a pronounced shift of capacity, particularly from the largest vessels, towards ports in the western Mediterranean, contrasting with the sharp contraction observed in several Eastern Mediterranean hubs. Within this broader East–West reconfiguration, ports such as Tanger Med illustrate how certain Western Mediterranean hubs were able to limit capacity losses, consistent with their geographical positioning and integration within carrier service networks. Overall, the study demonstrates the value of AIS data, as a strategic-monitoring tool, in documenting port-level manifestations of sudden geopolitical disruption, and supporting situational awareness for port authorities and operators, while acknowledging the limits of AIS-based inference regarding underlying strategic intentions.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:35:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2700883</guid>
    </item>
    <item>
      <title>Transshipment incidence, vessel-class composition, and carrier dominance: the footloose effect in container port throughput volatility</title>
      <link>https://trid.trb.org/View/2700582</link>
      <description><![CDATA[This study examines year-to-year container throughput volatility in 170 global ports between 2014 and 2024, with a focus on the footloose nature of transshipment cargo. This period is marked by major disruptions, including supply-chain congestion and geopolitical tensions. A volatility index is developed to assess how three operational drivers shape this instability: transshipment incidence, vessel-class composition, and dominant-carrier market share. Ports are grouped into low-, medium-, and high-intensity clusters for each indicator. The results reveal three main patterns. First, higher transshipment incidence is associated with greater volatility, although regional differences persist. Second, ports with balanced participation of large and feeder vessels, characteristic of hub-and-spoke systems, exhibit the highest volatility, with mainline services being more fluctuation-prone than feeder operations. Third, greater market-share concentration in a single carrier increases exposure to strategic decisions, intensifying volatility. Overall, the findings show how specific operational configurations amplify the footloose nature of transshipment traffic, providing new evidence on the drivers of port-level volatility.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2700582</guid>
    </item>
    <item>
      <title>Bridging theory and practice: lessons from the first automated container terminal in Indonesia</title>
      <link>https://trid.trb.org/View/2700880</link>
      <description><![CDATA[Port automation has emerged as a transformative force across the global maritime industry, promising substantial advantages over conventional ports. While literature on port automation is extensive, relatively limited studies address the practical challenges of implementation in developing economies beyond the design stage. This study bridges this gap by linking established port automation literature with an in-depth case study of Teluk Lamong Terminal (TTL), Indonesia’s first automated container terminal and a pioneering example in a developing economy context. The study combines a systematic literature review with qualitative evidence from seventeen in-depth interviews spanning staff, middle managers, top management, and external stakeholders to identify key dimensions of implementation. The findings show that while automation at TTL has delivered improvements in performance, safety, and process standardization, these outcomes emerged through continuous adjustment. Workforce roles were reconfigured toward more advanced planning and monitoring, while governance arrangements evolved iteratively as operational experience accumulated. In theoretical terms, the TTL case supports socio-technical and interpretive perspectives that emphasize co-evolution between technology, organization, and governance. Meanwhile, the waterfall or linear maturity models are challenged, as the automation at TTL functioned as an ongoing organizational condition. The analysis further shows that the prevailing automation literature tend to underestimate the temporal horizon, managerial discretion, and institutional capacity required to stabilize automation in specific developing economy settings. By explicating these mediating mechanisms, this study refines existing automation theory and provides empirical insights for ports in similar developing countries pursuing automation under conditions of institutional and organizational constraint.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2700880</guid>
    </item>
    <item>
      <title>Simulation to predict the behaviour of a new seaport gate: An application in the Sines container terminal</title>
      <link>https://trid.trb.org/View/2730301</link>
      <description><![CDATA[With rapid technological changes and globalized supply chains, intelligent and adaptable logistics systems are crucial, particularly at seaports. Container terminals face increasing volumes and demands, leading to potential truck congestion and long queues. Automation offers a solution by optimizing port performance and adapting to dynamic conditions, though port digitalization is complex due to diverse stakeholders and information flows. This work is part of the digital transformation of the Port of Sines - Portugal, specifically in the Smart Gate concept context. For this purpose, the Port of Sines Administration, together with other partners, has established the prerequisite of a pre-gate, which is currently being analysed from the point of view of its processes, the technologies to be included and the information models to be supported. This work aims to dynamically analyse the pre-gate model by testing different scenarios to propose improvements to the inherent processes using simulation. The study culminated in a discrete-event simulation model with Anylogic software to predict pre-gate behaviour and foresee potential issues. By testing various scenarios and evaluating their effects on key performance indicators, this model supports the continuous improvement of pre-gate processes.]]></description>
      <pubDate>Mon, 24 Aug 2026 16:48:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730301</guid>
    </item>
    <item>
      <title>The evolution of port-hinterland relations: A systematic review of governance practices</title>
      <link>https://trid.trb.org/View/2724954</link>
      <description><![CDATA[Port-hinterland relations have changed profoundly due to containerization, globalization, and the growing integration of transport and logistics networks. Yet, the key question if governance has adapted to the evolving port-hinterland relations remains insufficiently answered. Although previous studies document the transformation of port–hinterland relations, existing research has not systematically examined how governance arrangements respond to these spatial and functional changes, leaving fragmented insights across disciplines. To address this gap, the study investigates the governance of port–hinterland connectivity through the analytical lens of what is governed, who governs, how governance is organized, and why particular arrangements are pursued. The authors do so by conducting a systematic literature review of 49 peer-reviewed articles, analyzing how governance domains, actors, mechanisms, and rationales are conceptualized and how these relate to the evolution of port–hinterland systems. The review shows that governance has diversified in response to system changes, highlights the importance of temporal and spatial context in understanding these developments, and provides a conceptual foundation for future research on port–hinterland governance.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724954</guid>
    </item>
    <item>
      <title>Integrated optimization for truck arrival management and yard capacity utilization in a container terminal</title>
      <link>https://trid.trb.org/View/2720902</link>
      <description><![CDATA[Truck congestion in a container terminal yard is usually caused by the imbalance between the arrival demand of external trucks and the supply of yard service capacity. In the absence of infrastructure expansions, the terminal yard congestion can only be mitigated by optimal control of truck arrivals and the management of yard resource allocation. This paper proposes an integrated modelling framework that mitigates terminal yard congestion by joint optimization of the truck arrival demand and the utilization of yard capacity, subject to truck arrival demand shifting, yard queueing, and yard resource allocation constraints. This integrated model consists of a truck demand management model that manages the distribution of truck arrivals over a planning horizon, a descriptive queueing model that captures the dynamics of truck arrival and service processes, and a yard capacity utilization model that controls the yard resource configuration and the consequent inbound and outbound truck service rates over time. For solving the integrated model, we develop a tailored iterative solution method that decomposes the demand and supply decisions and effectively strikes the balance between truck throughput and yard congestion level. A core contribution of this approach is the interaction between an expansion rule, which iteratively enriches the master problem’s restricted service option set with service rate configurations identified by the subproblem, and a dynamic update rule that strictly tightens the master problem’s congestion threshold. This mechanism is novel in the sense that it offers a convergent and tractable approach to coordinate the demand and supply management decisions that used to be intricate in the literature. We evaluate the computation performance and solution quality of the integrated framework on instances generated from the operational data of a large container terminal. Based on the computation results, we provide insights into truck demand and service management for congestion mitigation in a container terminal.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720902</guid>
    </item>
    <item>
      <title>Neural network-enhanced branch-and-bound algorithm within an event-driven rolling-horizon framework for the berth allocation problem</title>
      <link>https://trid.trb.org/View/2734407</link>
      <description><![CDATA[The continuous growth in container transport demand has placed increasing pressure on port operations, intensifying conflicts between vessel arrivals and limited quay resources. This study investigates the dynamic continuous berth allocation problem (DCBAP) under time-varying vessel arrival information. Specifically, an event-driven rolling-horizon optimisation strategy is developed, in which vessel information updates serve as event triggers to decompose the original problem into a sequence of stage-based subproblems for successive rescheduling. Computational experiments show that, compared with the non-rolling planning scheme and the time-driven rolling-horizon strategy, the proposed event-driven rolling-horizon strategy achieves clear improvements in both computational time and berth allocation results. To further improve solution efficiency, a neural network (NN) is integrated into the BB algorithm through two frameworks: search optimisation and initial bound optimisation. Compared with the basic BB algorithm, the proposed NN-enhanced BB frameworks improve solution quality by up to 3.23%. Under the tested experimental conditions, the proposed method also outperforms the commercial solver Gurobi, with the maximum improvement reaching 9.8%. Overall, the proposed method provides an intelligent optimisation approach for the DCBAP that combines dynamic adaptability with computational efficiency.]]></description>
      <pubDate>Tue, 11 Aug 2026 16:35:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2734407</guid>
    </item>
    <item>
      <title>Port decarbonisation readiness of container Terminals: An Interval-Valued fuzzy TOPSIS approach</title>
      <link>https://trid.trb.org/View/2737138</link>
      <description><![CDATA[Ports are essential elements of global supply chains, yet their progress towards decarbonisation varies widely. This study develops a Port Decarbonisation Readiness Framework (PDRF) to evaluate the readiness of six container terminals in Türkiye. The framework integrates three components: entropy-based weighting of sustainability indicators, interval-valued fuzzy representation of uncertainty, and a fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) ranking procedure. The PDRF assesses terminal-level readiness across Scope 1 and 2 operational emissions and Scope 3 mitigation readiness for onshore power supply (OPS). Because Scope 3 emissions from shipping operations are difficult to quantify, they are represented through mitigation-readiness indicators (OPS planning and electrification potential) rather than a full inventory. The results reveal substantial differences in decarbonisation readiness among terminals and demonstrate how the PDRF can support port authorities and policymakers in identifying priorities and aligning port strategies with climate goals.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:23:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737138</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>Artificial intelligence in ports: a bibliometric and evolutionary perspective</title>
      <link>https://trid.trb.org/View/2698267</link>
      <description><![CDATA[Container terminal operations are rapidly adopting artificial intelligence (AI) technologies to improve efficiency, sustainability, and automation amid ongoing digital transformation. This study conducts a comprehensive bibliometric analysis of 391 publications (1992-2024) from the Web of Science Core Collection using Biblioshiny 4.0. The findings reveal a paradigm shift toward AI-driven optimisation across key areas, including berth allocation, crane scheduling, truck fleet management, and intelligent port automation. Despite this progress, a critical gap remains: the lack of empirical validation using real operational data. This gap highlights the urgent need for cross-industry collaboration to bridge theory and practice. The study urges policymakers and port authorities to implement standardised AI frameworks, invest in workforce upskilling, and enhance digital infrastructure. Future research should focus on scalable, data-validated AI applications and on promoting longitudinal case studies and industry partnerships to ensure the effective, sustainable integration of AI technologies into port logistics.]]></description>
      <pubDate>Wed, 29 Jul 2026 09:16:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698267</guid>
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