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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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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      <title>Simulation-Based Digital Twins for Internal Transport Systems</title>
      <link>https://trid.trb.org/View/2648637</link>
      <description><![CDATA[The increasing complexity of internal transport systems in industrial applications poses significant challenges for operational decision-making. This paper presents a simulation-based Digital Twin framework developed within the research project TwinTraSys to support the control of such systems. The framework is specifically tailored to the constraints of real-world IT infrastructures, requiring minimal transaction data, while enabling predictive analysis and dynamic scenario evaluation. It consists of a modular architecture divided into four core components: data provision, data preparation, simulation, and decision support. A dedicated simulation framework enables the automated generation of structural models and the integration of real-world operational control systems. The proposed approach has been validated in industrial settings and has demonstrated its ability to support resource planning and transport resource allocation through simulation-based experimentation and multi-criteria evaluation. This paper contributes to the practical advancement of Digital Twin applications in intralogistics by bridging the gap between theoretical models and real-world constraints.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648637</guid>
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      <title>A Group Decision-Making Based Spherical Fuzzy MCDM Approach for Smart Airports</title>
      <link>https://trid.trb.org/View/2648627</link>
      <description><![CDATA[Recent technological developments have changed the business landscape in the aviation industry. The widespread use of advanced digital technologies has transformed customers’ and passengers’ behaviors and expectations, as well as the future trajectory of the industry. The aviation ecosystem is characterized by its complexity, comprising numerous components, players, and systems. Within this framework, airports hold significant importance as they function as critical junctions for all components, stakeholders, and participants involved in the industry. The smart airport concept has become a buzzword with the integration of digital technologies into airports to offer innovative solutions and seamless passenger experiences. At this point, it is critical to understand the characteristics of a smart airport. However, transitioning to a smart airport is a strategic decision-making process that requires considering multiple criteria. Therefore, this study aims to identify and analyze the characteristics of smart airports to inform future strategies and roadmaps for decision-making. It emphasizes the importance of technology selection and investment planning through a group decision-making (GDM) approach. The study systematically collects characteristics through a comprehensive literature review and the insights of three decision-makers (DMs) who possess expertise in the industry. To evaluate the suitability, accuracy, and validity of these characteristics, the research utilizes the Spherical Fuzzy Analytic Hierarchy Process (SF AHP) methodology within the context of an airport in Turkey. The study findings indicate that technology infrastructure plays a crucial role in smart airports. Additionally, cybersecurity, interactivity, human-machine collaboration, connectivity, and technology strategy should be prioritized within the smart airport ecosystem.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648627</guid>
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      <title>Modeling Methods for the Environmental Impact Assessment of Shared Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2648576</link>
      <description><![CDATA[Shared Autonomous Vehicles (SAVs) are gaining attention as a promising solution to reduce the environmental footprint of the transportation sector. However, their actual impact remains uncertain and depends on key variables such as energy source, vehicle occupancy, and deployment strategies. This paper presents a systematic review of modeling approaches used to assess the environmental impacts of SAVs, focusing on energy consumption and greenhouse gas (GHG) emissions. Based on the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR‑4‑SLR), this review finds that agent-based models dominate the field, offering fine-grained insights into user behavior, fleet dynamics, and network interactions. Emerging systemic methods (e.g., integrating LCA, grid and land‑use modeling) aim to extend analysis beyond operational emissions. While many studies report substantial emission reductions under ideal conditions, outcomes remain highly sensitive to assumptions on electrification, adoption rates, and regulatory frameworks.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648576</guid>
    </item>
    <item>
      <title>CBRNE Threats in Civilian Airports: security, detection technologies and crisis management</title>
      <link>https://trid.trb.org/View/2648567</link>
      <description><![CDATA[This article analyzes CBRNE threats (chemical, biological, radiological, nuclear, and explosive) within the context of the security of civilian airports. It presents an overview of detection technologies, current operational challenges, and the concept of integrated crisis management systems. The potential use of artificial intelligence and predictive analytics in mitigating such threats will also be discussed. The conclusions offer recommendations for implementing harmonized technological and organizational solutions and for enhancing interoperability among airport security services.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648567</guid>
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    <item>
      <title>Enhancing logistics through the development of port infrastructure: the role of truck parking facilities</title>
      <link>https://trid.trb.org/View/2648561</link>
      <description><![CDATA[The increasing volume of container handling at seaports leads to a rise in heavy vehicle traffic, often resulting in congestion that poses a significant challenge to port competitiveness by disrupting operational fluidity, increasing logistics costs and delays. This study presents the development of an agent-based simulation model designed to evaluate the impact of incorporating a truck parking facility on the hinterland operations. The model is adaptable to diverse maritime supply chain and utilizes performance indicators, such as queue length and total cycle time. A case study conducted at Itapoá Port, Brazil, indicates that the infrastructure can be used as an efficient congestion management strategy, since it has the potential to reduce the total cycle time of full containers by 15.69% for import flows and 18.43% for export flows. Findings suggest that the proposed solution can mitigate congestion, enhance operational efficiency, and contribute to more sustainable port logistics.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648561</guid>
    </item>
    <item>
      <title>Oil pipeline leaks: modelling and design of innovative plant solutions based on free floating sensor systems</title>
      <link>https://trid.trb.org/View/2648554</link>
      <description><![CDATA[The paper addresses the problem of leaks in pipelines used to transport crude oil or refined petroleum products over long distances in petroleum industrial plants. The article refers to a pipeline monitoring system based on the latest generation sensors which, flowing inside the pipes together with the transported fluid, are able to record the acoustic emissions generated by any leaks and locate them. The paper proposes an optimization model to size a monitoring service of this type for a given pipeline in order to guarantee specific performance such as monitoring frequency, timely data acquisition, and sensor battery/memory limits while minimizing costs. The model outputs the minimum number of sensors required and the sequence of insertion and withdraw points for each sensor. All withdraw points are points where the data recorded in the sensor in the previous route is read. Some of the withdraw points may be charging points, if the sensor battery is not enough to cover the next trip. Relocation trips, required to move a sensor from the withdraw point to the next insertion point and performed by an operator were also taken into account. The proposed algorithms were tested on a network of examples and the results obtained confirmed that the optimization model is able to minimize the number of deployed devices under realistic constraints, demonstrating the feasibility and scalability of the optimization methodology.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648554</guid>
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    <item>
      <title>Trends in intermodal transport: A bibliometric analysis of recent literature (2020-2024)</title>
      <link>https://trid.trb.org/View/2648530</link>
      <description><![CDATA[This paper presents a structured review of 137 peer-reviewed journal articles on intermodal transport design published between 2020 and 2024. The aim of the review is to highlight current research trends, thematic developments, and recent innovations within the field. A bibliometric and content-based analysis was conducted, including the evaluation of publication outlets, authorship patterns, funding sources, and keyword dynamics. Results show that academic interest in intermodal transport has remained stable in recent years, with strong attention to environmental concerns, and that collaborations among researchers have increased in time. The keyword analysis revealed a core set of well-established topics related to sustainability, transport planning, and simulation/optimization techniques, alongside a growing number of emerging terms focused on resilience, digital transformation, and technological innovation. Citation data further highlight the relevance of ICT-related themes and maritime transport. Overall, the study outlines a research domain that is both consolidated and in evolution, with future directions pointing toward digitalization and resilience of intermodal systems in uncertain environments. In line with these findings, the role of digitalization, resilience and simulation/optimization techniques could be deepened in future research activities.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648530</guid>
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    <item>
      <title>The Research Landscape of Data-Driven Simulation in Transport and Logistics</title>
      <link>https://trid.trb.org/View/2648139</link>
      <description><![CDATA[Data-driven simulation is a fairly new trend in the development of computer modeling technologies and related applications in the field of transport and logistics. The main applications cover urban mobility and traffic management, harbor and maritime logistics, airport and airside operations management, and warehouse and supply chain management. Despite growing interest and developments, the field of data-driven simulation in transportation and logistics remains fragmented across sub-disciplines and applications. The aim of this paper is to review the evolution, current status and future trends of research in this field through a comprehensive analysis of the relevant literature and recent technological advances. The role of artificial intelligence in advancing data-driven simulation is discussed, in particular the use of digital twins of real logistics systems to operate in real time and fill gaps in incomplete data sets.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648139</guid>
    </item>
    <item>
      <title>CPDGen: a Scalable Synthetic Dataset Generator for Container Port Operations</title>
      <link>https://trid.trb.org/View/2648115</link>
      <description><![CDATA[Obtaining data for developing port studies and applications is awkward and costly. However, extending the basis of research in the field would be beneficial to improve its growth. Thus, we propose CPDGen (Container Port Dataset Generator), a synthetic data generator developed for container port simulation and experimentation, using a modular architecture which mimics the flow in a port. The implementation, in Python, is based on the SimPy framework and organizes the simulation process into five main phases: configuration, entities, actions, monitoring, and testing. Configuration is automatically performed processing simple JSON files, through which the end user can establish the infrastructural, temporal, and strategic parameters. The stochastic ship arrival engine implements the arrivals according to exponential, uniform, or weekly strategies, and the associated containers are dynamically generated by sampling characteristics from user-defined distributions. The simulation cycle manages the entire operational flow of arrival, mooring, unloading, and output updating. The monitoring module processes tables and graphs related to simulated ships and containers, while tests verify the consistency of the computed results. The modular division of the code allows for scalability, reusability, and extensibility. The tool has been designed to be integrated into experimental pipelines aimed at evaluating decision-making policies and generating synthetic benchmarks.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648115</guid>
    </item>
    <item>
      <title>Analysis of Automotive Sector Import Networks at the Port of Manzanillo, Mexico.</title>
      <link>https://trid.trb.org/View/2648113</link>
      <description><![CDATA[Manzanillo, Mexico’s premier container port and a major hub in Latin America, boasts specialized terminals and equipment for diverse cargo, including roll-on/roll-off (Ro-Ro) for vehicles. While numerous global automotive brands (e.g., Nissan, Toyota, GM, Ford, VW, BMW, Audi, Honda, Kia, Mercedes-Benz) operate manufacturing plants in Mexico, some also use Manzanillo to import parts and components for their Mexican assembly lines. This paper analyzes automotive imported products through Manzanillo using a complex network tool. This method identifies the main countries exporting these items to the port, as well as their associated economic communities, and explores how this could benefit Mexico.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648113</guid>
    </item>
    <item>
      <title>Implementation of an Operational Capability Model: Case Study on Offshore Support Vessel Fleet Management</title>
      <link>https://trid.trb.org/View/2648105</link>
      <description><![CDATA[This paper presents a numerical implementation of an operational capability model for selecting an Offshore Service Vessel (OSV) within a feet, based on mission requirements. The initial model, grounded in dynamical systems theory, represents the system state as a labeled multigraph with attributes on its vertices. It is extended to weighted edges to provide a more pertinent representation of the OSV’s operational state. The evolution of the system is formalized using algebraic operations, which were initially developed for simple graphs and are extended to multigraphs in the present paper, giving it a discrete nature. A UML diagram is proposed to ensure the reproducibility of the implementation.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648105</guid>
    </item>
    <item>
      <title>Modeling sustainable pedestrian mobility on long-distance trails: a System Dynamics approach</title>
      <link>https://trid.trb.org/View/2648100</link>
      <description><![CDATA[The growing global interest in long-distance pedestrian trails, particularly in contexts rich in cultural and natural heritage, necessitates a comprehensive understanding of their complex dynamics to optimize soft mobility and, above all, pedestrian mobility. Traditional transport engineering approaches often focus on optimizing individual elements in a fragmented manner, overlooking the intricate feedback loops that govern visitor experience and resource management. This paper proposes a novel application of the System Dynamics (SD) approach to model the interrelationships influencing pedestrian mobility along long-distance trails. We develop a Causal Loop Diagram (CLD) that qualitatively maps key variables, including factors such as infrastructure capacity and walker satisfaction. This framework considers the various impacts – environmental, social, and economic – ensuring a holistic perspective on trail management and development, while implicitly supporting sustainable practices. The developed CLD serves as a foundational qualitative model, identifying critical feedback loops that can drive the evolution of long-distance trail systems. It highlights the non-linear effects of variables like infrastructure saturation on both walker satisfaction and the overall functionality of pedestrian routes. This systemic perspective is crucial for transport experts and policymakers aiming to design resilient and efficient soft mobility solutions that enhance pedestrian experiences while managing resources effectively. Ultimately, this CLD provides a robust conceptual framework as the direct basis for subsequent Stock & Flow Diagram (SFD) development and quantitative simulation studies, enabling the evaluation of different policy interventions and management strategies for long-distance pedestrian mobility.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648100</guid>
    </item>
    <item>
      <title>Enhancing Maritime Container Logistics through Optimized Repair Scheduling</title>
      <link>https://trid.trb.org/View/2648099</link>
      <description><![CDATA[Container repair workshops are crucial for sustaining global supply chains, yet they face significant challenges, including capacity shortages and repair complexities, which lead to delays in container availability and disrupt the seamless flow of goods. This study addresses these issues by introducing a mixed integer linear programming model to optimize task scheduling and worker assignments in such workshops, aiming to minimize the makespan while adhering to technological precedence constraints. Computational experiments using the AIMMS software on various test instances, designed based on realistic datasets, highlight the model’s effectiveness, demonstrating robust performance in small to moderate problem sizes, with solution times ranging from 0.01 seconds for small instances (5 tasks, 3 workers) to 0.98 seconds for moderate instances (20 tasks, 7 workers). This study underscore the potential for cost savings and enhanced supply chain resilience through improved operational efficiency, such as minimizing idle times and optimizing resource allocation to achieve efficient throughput, as illustrated in the case study for a 35 task scenario. This work advances container logistics research and equips supply chain executives with a data driven tool to address capacity shortages and streamline repair operations.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648099</guid>
    </item>
    <item>
      <title>A DRL approach for last-mile delivery with robot fleets</title>
      <link>https://trid.trb.org/View/2648088</link>
      <description><![CDATA[In recent years, the optimization of delivery systems in urban environments has gained increasing interest, particularly due to advancements in Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques. This paper concerns the application of Deep Reinforcement Learning (DRL) algorithms within cooperative multi-agent frameworks for optimising urban delivery systems using realistic traffic simulations (e.g., SUMO). The existing research explores the use of RL or DRL for routing or planning individual vehicles but lacks a detailed evaluation of how cooperative multi-agent training affects system-wide efficiency, beyond the performance of individual agents. This work explores the application of DRL algorithms in a multi-agent simulation environment, with the goal of improving the efficiency of parcel deliveries. Using Simulation Urban Mobility Environment, a traffic simulation platform, a model is developed, specifically an autonomous delivery system where each vehicle cooperates to optimise the delivery tasks. The focus on the cooperative approach, examining in detail the effect of agents’ training on delivery time and operational costs. The selected approach has a significant impact on delivery efficiency and represents a promising solution to optimise autonomous transport systems.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648088</guid>
    </item>
    <item>
      <title>The Safety of Offshore Structures: Key Challenges, and International Regulatory Frameworks</title>
      <link>https://trid.trb.org/View/2648087</link>
      <description><![CDATA[Maritime structures play an important part not only in the global economy but also in the development of the green energy industry. Their safety is a priority due to their exposure to such a dynamic and unpredictable environment. Extreme conditions, the impact of climate change, corrosion, and material degradation caused by constant exposure to salt water are the main challenges that can affect the integrity and proper functioning of maritime structures. Another major issue is the ecological impact and pollution, especially in the case of oil spills or other chemical spills that could have long-term consequences on marine ecosystems. This paper provides a comprehensive analysis of the main types of offshore structures employed in energy extraction and production, highlighting their significance in the context of the growing demand for renewable energy sources. Additionally, it thoroughly examines the safety issues within the maritime domain, emphasizing the need for enhanced regulations and innovative solutions to mitigate risks associated with offshore operations. Furthermore, the paper presents a detailed analysis of the total number of incidents occurring between 2013 and 2026, alongside an evaluation of the fatality and injury rates. It also underscores the importance of international regulatory frameworks that impose high requirements on their design, construction, and maintenance to sustain the safety and longevity of maritime structures.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648087</guid>
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