<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <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" />
    <description></description>
    <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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Digital Twin for resilience and sustainability assessment of port facility</title>
      <link>https://trid.trb.org/View/2709488</link>
      <description><![CDATA[This paper addresses key gaps in Digital Twin research for port operations. It first develops an integrated Digital Twin model for dry bulk terminals, focusing on biomass handling, an area largely overlooked in existing studies. Second, it introduces Building Information Modeling (BIM) to offer a holistic perspective on port sustainability, particularly in relation to energy use and building-related emissions. Third, it tackles the challenge of limited high-resolution data by employing synthetic data and heuristic search methods to calibrate the Digital Twin in the absence of extensive IoT infrastructure. A novel, generic framework is proposed to support resilience and sustainability assessments across various cargo types and terminal facilities. This framework is demonstrated through a working Digital Twin for biomass operations at the Port of Tyne. The study contributes to advancing port Digital Twin technologies and offers new insights for future research in bulk cargo logistics and sustainable port development.]]></description>
      <pubDate>Wed, 26 Aug 2026 10:13:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709488</guid>
    </item>
    <item>
      <title>An Application of Risk Analysis in Liquid Cargo Terminals for Occupational Health and Safety Management</title>
      <link>https://trid.trb.org/View/2742726</link>
      <description><![CDATA[Liquid cargo terminals present numerous risks related to occupational health and safety (OHS). These OHS risks may result from the cargo handled and the services provided. Risks must be eliminated or maintained at an acceptable level to ensure a safe working environment in liquid cargo terminals. This study aims to identify risks in liquid cargo terminals, conduct risk analysis using different methods for the identified risks, and determine which method is most suitable for liquid cargo terminals among those used in risk analysis. Analytic Hierarchy Process (AHP), fuzzy AHP, 5x5 L-type matrix, and Fine Kinney methods were used for risk analysis. The advantages and disadvantages of each method were identified and compared. According to the risk analysis, “environmental pollution and spatial problems,” a sub-criterion of the unloading operation, was calculated as the most important in the AHP method. In contrast, “damaged tanks, tank containers, or IBC tanks” was identified as the most important in the fuzzy AHP method. In both AHP and fuzzy AHP analyses, “liquid chemical cargoes” were the riskiest type of cargo. “Generation of static electricity” was calculated as the hazard with the highest risk score in the 5x5 L-type matrix and Fine Kinney methods. In these methods, “crude oil-oil derivatives” had more hazards at very high-risk levels than other types of cargo when compared by load type. A combination of the Fine Kinney and fuzzy AHP methods is recommended for liquid cargo terminals. The available literature reveals a significant lack of comprehensive and holistic risk analysis studies on liquid cargo terminals. Therefore, this study contributes to the literature by identifying liquid cargo terminal risks and comparing the risk analysis methods used.]]></description>
      <pubDate>Thu, 06 Aug 2026 09:08:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742726</guid>
    </item>
    <item>
      <title>A review of bulk terminal operations: Status, trends, and future outlook</title>
      <link>https://trid.trb.org/View/2577282</link>
      <description><![CDATA[Bulk terminals are key infrastructures in the supply chain because they are the main nodes responsible for connecting sea and land transportation of bulk cargo. Their efficient management requires optimizing terminal operations, a challenge that has increasingly attracted researchers’ attention and motivates the need to review and analyze the work done so far. While research in container terminals has been widely reviewed in multiple surveys, the same does not apply to bulk terminals, for as yet there is no equivalent compilation or review of the existing research. This work addresses the gap by providing the first systematic literature review on the optimization of seaside and yardside operations at bulk terminals. It covers 120 research papers on dry and liquid bulk terminals. Keyword network analysis is used to establish relations within the existing work and identify five predominant research streams: seaside operations optimization, yardside operations optimization, simulation of terminal operations, integrated operations optimization, and artificial intelligence-based approaches. The papers are classified according to the type of port, type of terminal, type of cargo, problem(s) addressed, performance measures, and solution methods used. Furthermore, this review goes beyond reviewing and classifying the existing research; it also identifies research limitations and outlines several promising directions for future research, providing valuable insights to researchers and practitioners.]]></description>
      <pubDate>Tue, 09 Sep 2025 09:30:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577282</guid>
    </item>
    <item>
      <title>Berth Allocation Problem in Export Tidal Bulk Ports with Inventory Control</title>
      <link>https://trid.trb.org/View/2407030</link>
      <description><![CDATA[This paper presents the problem of allocating berth positions for vessels in tidal bulk port terminals (BAPTBI), considering the specific export scenario and robust control over goods’ stock levels. An integer linear mathematical model is proposed for the discrete case (time and quay). The model controls the minimal and maximum inventory levels. A dataset of 59 instances was generated based on data obtained from a relevant bulk terminal in São Luís, Brazil. Through the experiment using the Gurobi solver, it is noticed that some medium-sized instances take more than 1 h to be solved.]]></description>
      <pubDate>Tue, 22 Jul 2025 10:32:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407030</guid>
    </item>
    <item>
      <title>Dry bulk terminal operations: A critical review of models and optimization methods</title>
      <link>https://trid.trb.org/View/2567367</link>
      <description><![CDATA[Dry bulk terminals (DBTs) are critical interfaces that facilitate the transfer of large volumes of bulk commodities from natural sources to the global economy. Efficient operations at these terminals enhance local industry competitiveness, optimize regional trade balances, and contribute significantly to global economic growth. With the expansion of dry bulk trade and the rising demand for more efficient, resilient, and environmentally sustainable operations, traditional human-reliant decision-making processes are increasingly being supplanted by automated and optimized systems. However, understanding of DBT operations lags behind that of container terminals, which garnered full scholarly attention much earlier. This study provides the first comprehensive review of the literature on DBT operations and proposes future research directions to close this gap. The authors focus on DBTs’ unique features related to facility layout and operational flow, supply chain integration, and environmental aspects such as dust pollution and energy consumption. The authors thoroughly analyze the operational features, assumptions, optimization objectives, and problem modeling in the literature to reveal the progress, focal points, and limitations of relevant studies. This paper identifies key drivers of current research trends and outlines several critical future research directions essential for the development of DBTs, including pollution-free and resource-efficient operations, optimization under uncertainties, emerging technology-facilitated operations, and operations coordination in dry bulk transportation networks. This review should help researchers and practitioners in the field of ports and shipping systematically and comprehensively understand the current status of and key issues in the development of DBTs.]]></description>
      <pubDate>Fri, 11 Jul 2025 14:28:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2567367</guid>
    </item>
    <item>
      <title>Iqaluit’s New Deep-Sea Port in the Canadian Arctic</title>
      <link>https://trid.trb.org/View/2559466</link>
      <description><![CDATA[Iqaluit, located in Nunavut in the Canadian Arctic, is the site of a recently constructed full-serviced deep-sea port dedicated to the annual resupply of cargo, including dry cargo and bulk fuel. The port was built by the Government of Nunavut. Iqaluit, the capital city of the Nunavut territory in northern Canada, has a population of 8,000 and receives more cargo than any other Arctic community in Canada, typically receiving an average of 12 dry cargo ships and 7 fuel tankers per year during the navigable season of early July till the end of October. Other vessel calls to Iqaluit include the Canadian Coast Guard, fishing trawlers, research vessels, cruise ships, and naval ships. With a vertical tidal range approaching 12 m between high and low astronomical tides during the spring tides, dry cargo was historically lightered ashore by flat bottom barge to a beach accessible for only short daily windows, fuel was pumped ashore with floating hoses, and crew/passenger ship changes were tendered ashore in small boats. The new port includes a deep-sea wharf, sealift ramp, laydown yard, fuel pipeline extension and manifold, and other ancillary components. The newly completed facility allows for 24 h operations, no tidal restrictions, and increased laydown space resulting in significant reductions in the unloading times of vessels.]]></description>
      <pubDate>Fri, 27 Jun 2025 11:04:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559466</guid>
    </item>
    <item>
      <title>The Role of Physical Modeling in a Port Expansion Project in a Complex Wave Environment—Matarani Port, Peru</title>
      <link>https://trid.trb.org/View/2559418</link>
      <description><![CDATA[This paper discusses the use of physical modeling in designing the expansion of Matarani Port, Peru, by Terminal Internacional Del Sur S.A. (TISUR). The expansion includes a new general cargo terminal (Terminal G) and modifications to an existing terminal (Terminal F) facing excessive ship motions. Matarani Port, influenced by complex wave patterns due to nearby cliffs and irregular bathymetry, posed challenges for current numerical tools in predicting wave impacts on moored vessels. The physical model study, conducted at the National Research Council Canada’s (NRC) lab in Ottawa, Canada, involved two phases. The first phase tested moored ship responses to ensure operational efficiency and proposed terminal modifications, including new mooring configurations and a sheltering breakwater. The second phase evaluated breakwater stability under extreme conditions. Novel dynamic mooring simulators were developed and used to model constant tension mooring elements, improving the accuracy of predictions regarding ship motions and structural stability, and are a focus of this research paper. The study’s findings were crucial for optimizing designs and validating the port’s expansion plans, ensuring resilience and cost-effectiveness.]]></description>
      <pubDate>Mon, 23 Jun 2025 15:53:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559418</guid>
    </item>
    <item>
      <title>Deep Reinforcement Learning for Channel Traffic Scheduling in Dry Bulk Export Terminals</title>
      <link>https://trid.trb.org/View/2449243</link>
      <description><![CDATA[Heavy navigation demands of incoming/outgoing ships and operational features in dry bulk export terminals (DBETs) call for effective and intelligent optimization methods to improve navigation channel traffic and reduce delays. Considering ship deballasting delays in DBETs and their influences on channel traffic flow, this paper proposes a channel traffic scheduling (CTS) optimization method based on deep reinforcement learning (DRL). The CTS problem is formulated into a Markov decision process with tailored state and action definitions. Practical constraints, such as tidal windows and dynamic switching traffic mode, are incorporated into action selection processes. In coping with large state-action spaces caused by practical applications, a hierarchical DRL framework is proposed to perform layered decision-making. Relying on the reward signal design, DRL agents can learn optimization policies to produce integrated scheduling plans of channel traffic and ship deballasting operations while minimizing ship mooring, unberthing, and deballasting delays. A proximal policy optimization method is developed for coupling training DRL agents. Numerical experiments demonstrate that the proposed method can converge faster to better scheduling strategies and efficiently generate high-quality CTS solutions for industrial-scale applications.]]></description>
      <pubDate>Fri, 28 Feb 2025 16:43:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2449243</guid>
    </item>
    <item>
      <title>Predicting cargo handling and berthing times in bulk terminals: A neural network approach</title>
      <link>https://trid.trb.org/View/2480447</link>
      <description><![CDATA[This paper presents a comprehensive study on the development of a neural network model aimed at predicting the cargo handling time and berthing time. Utilizing physical ship data (length, beam, draught, DWT, and GT), cargo type, daily weather conditions, cargo handling equipment data, and historical operation times, the model aims to enhance the operational efficiency of bulk terminals. A case study conducted at a bulk terminal, leveraging a three-year dataset, serves as the foundation of this research. The outcomes of the neural network analysis highlight the average cargo handling capacity under various conditions, providing crucial insights for port operation optimizations such as determining the optimal number of gangs, calculating berth occupancy ratios, and improving berth planning strategies. The implications of these findings are significant, offering a pathway toward more efficient and predictive port management strategies, with the potential to substantially reduce operational costs and increase throughput efficiency. This study not only contributes to the existing body of knowledge by integrating diverse data types into a predictive model but also proposes practical applications that can lead to more informed decision-making in port and terminal operations.]]></description>
      <pubDate>Wed, 22 Jan 2025 09:36:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2480447</guid>
    </item>
    <item>
      <title>New Petroleum Products Terminal Port of El-Dekheila, Alexandria, Egypt</title>
      <link>https://trid.trb.org/View/2217661</link>
      <description><![CDATA[The new MIDTAP Petroleum Products Loading Facilities, located on a "greenfield" site on the Mediterranean coast at the Port of El-Dekheila in Alexandria, Egypt, will handle both the liquid bulk and dry bulk products produced by the new MIDOR refinery located some 20 km away. These products include gasolines (regular and premium), diesel, jet fuel and petroleum coke. This presentation describes the facility and its operations and discusses the planning process and project delivery methods used to execute the project.]]></description>
      <pubDate>Thu, 01 Aug 2024 11:24:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2217661</guid>
    </item>
    <item>
      <title>Hub Network Design</title>
      <link>https://trid.trb.org/View/1972118</link>
      <description><![CDATA[Hub network design problems (HNDPs) lie at the heart of network design planning in transportation and telecommunications systems. Hub-based networks provide connections between many origins and destinations via hub facilities that serve as transshipment, consolidation, or sorting points for commodities. Hub facilities help to reduce the number of required arcs to connect all nodes and enable economies of scale due to the consolidation of flows on relatively few arcs. HNDPs can be seen as a class of multicommodity network design problems in which node selection decisions are taken into account. This chapter overviews the key features of hub networks, the types of decisions that are usually considered when designing them, and how these decisions interact between them. The authors describe commonly considered assumptions and properties and highlight how these impact the formulation and solution of various classes of HNDPs.]]></description>
      <pubDate>Mon, 20 May 2024 14:02:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/1972118</guid>
    </item>
    <item>
      <title>An Interactive Simulation System for Process System of Bulk Cargo Terminal</title>
      <link>https://trid.trb.org/View/2281541</link>
      <description><![CDATA[Process system optimization is one of the major contents of bulk cargo terminal construction. Computer simulation is an effective way to solve this problem. It can be found that the process system optimization is very complex because there are mutual interferences among the process flows, and it should be simplified. But the excessive simplification will make the simulation system deviate from the actual situation and the satisfied optimization results can not be obtained. Based on the construction of a simulation model for the process system of the bulk cargo terminal, human-computer interaction technology is introduced to establish an interactive simulation model. And a simulation system is developed with VC++ for an ore import terminal to validate the practical value of the model, which provides an effective lead-in to the analysis and optimization of the process system of a bulk cargo terminal.]]></description>
      <pubDate>Tue, 27 Feb 2024 16:03:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2281541</guid>
    </item>
    <item>
      <title>Scheduling of automated ore terminal operations based on fixed inflow rhythm</title>
      <link>https://trid.trb.org/View/2322895</link>
      <description><![CDATA[To facilitate transportation, some mines construct specialized ore terminals for outbound shipments. However, these terminals frequently encounter challenges when synchronizing their schedules with the production plans of the mines. The primary issue arises due to the inherent deviation between vessel demand and mine production schedules. The terminals must efficiently arrange storage positions for diverse cargo types and berthing orders of vessels to reduce the time vessels spend at the terminal and operation time. In this study, the authors propose a comprehensive two-step model framework that considers various customer priorities to optimize the operational plans for each operation. In the first step, they propose a column-generation approach to generate candidate berth plans. Subsequently, they evaluate the feasibility of berth plans and construct an infeasible CUT to modify the model. They implement the developed framework in real-world case studies to demonstrate its practicality and potential. Computational outcomes underscore the method’s efficacy in resolving coordination challenges between mines and terminals. Furthermore, it exhibits a noteworthy capability to diminish the terminal’s spatial requirements, enhancing its pragmatic value.]]></description>
      <pubDate>Mon, 22 Jan 2024 09:07:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2322895</guid>
    </item>
    <item>
      <title>Evaluating the Technical Efficiency of Dry-Bulk and General Cargo Terminals in Türkiye using Interval DEA</title>
      <link>https://trid.trb.org/View/2315143</link>
      <description><![CDATA[The efficiency of bulk solid and general cargo terminals, where ore, grain, and many raw materials are handled, plays a crucial role in socioeconomic development and lays the groundwork for reasonable trade costs. This study evaluates the relative performance of dry-bulk and general cargo terminals in Türkiye with interval data envelopment analysis (DEA). The dataset consists of 21 terminals operated by private companies for 2018-2021 and has been transformed into an interval form to apply interval DEA to imprecise data. The results imply that the efficiency levels of dry-bulk and general cargo terminals in the Marmara and Mediterranean tend to increase. It can be inferred that the average efficiency level of the Black Sea terminals has remained stable over the years, and the loss of efficiency in the Aegean is remarkable. The application of the Interval DEA in evaluating the efficiency of dry-bulk and general cargo terminals in the case of imprecise data can contribute significantly to the seaport efficiency literature.]]></description>
      <pubDate>Thu, 18 Jan 2024 11:37:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2315143</guid>
    </item>
    <item>
      <title>Service anomaly detection in dry bulk terminals: a machine learning approach</title>
      <link>https://trid.trb.org/View/2289227</link>
      <description><![CDATA[Bulk terminals are complex environments due to a number of variables that affect terminal performance. Although the analysis of big datasets is destined to become an important component of terminal management, previous research has not addressed this issue yet. This paper aims to shed new light on the operation of dry bulk terminals through a two-stage method based on unsupervised machine learning techniques. The first step gives an overview of the terminal's performance, revealing the strongest associations between the variables, while the second calculates an anomaly score for each vessel through an optimised implementation of the isolation forest. As a result, the author detects anomalous services which could be directly attributable to the terminal operator. This method can be used to increase transparency in service and assist the terminal operator and ship agents in future contracts.]]></description>
      <pubDate>Fri, 05 Jan 2024 14:27:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2289227</guid>
    </item>
  </channel>
</rss>