Maritime Transport Research最新文献

{"title":"Learning to predict trajectories with destinations from massive vessel data","authors":"Jing Sun ,&nbsp;Peng Wang ,&nbsp;Fanjiang Xu ,&nbsp;Zhaohui Liu","doi":"10.1016/j.martra.2025.100146","DOIUrl":"10.1016/j.martra.2025.100146","url":null,"abstract":"<div><div>Accurate and real-time ship trajectory prediction is a premise for high-stake tasks such as risk reduction, route planning, energy saving, etc., and becomes more feasible based on the processing of AIS data with sophisticated algorithms, so as to ensure high-standard navigation by providing efficient trajectory-based maritime traffic management. In contrast to current prevailing research striving to improve short-term prediction accuracy, this paper focuses on whereabouts estimation in order to improve longer-term predictions for vessels. Taking the meaningful whereabouts as implicit destinations, the novel Destination-Guided Trajectory Prediction (DGTP) model is proposed, which employs a cascaded Seq2Seq architecture with BiGRU to simultaneously predict both vessel destination and trajectory. Trajectory Alignment Loss (TAL) is also introduced to encourage precise matching between the predicted and true trajectories in optimizing the DGTP model. Experiments conducted on a large volume of AIS data demonstrate that both destination prediction and TAL loss can independently improve trajectory prediction performances. Moreover, the synergistic combination of destination prediction and TAL within the DGTP model leads to substantial accuracy enhancements, demonstrating the promising results in long-term prediction.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"10 ","pages":"Article 100146"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal and operational clustering of maritime container terminal activities for scenario-based truck appointment planning","authors":"Claudia Caballini ,&nbsp;Julio Mar-Ortiz ,&nbsp;Maria D. Gracia","doi":"10.1016/j.martra.2026.100148","DOIUrl":"10.1016/j.martra.2026.100148","url":null,"abstract":"<div><div>In today’s uncertain and rapidly evolving global landscape, maritime container terminals are increasingly affected by operational disruptions such as yard congestion, unbalanced resources utilization and delays in container handling. Truck Appointment Systems (TAS) have emerged as a key strategy to regulate truck arrivals and smooth peak demand, yet their effectiveness remains limited by insufficient integration with yard-side dynamics. Appointment allocation is typically designed without accounting for the spatial and temporal variability of yard operations. Designing a robust TAS requires the definition of realistic and representative operational scenarios. This paper proposes an enhanced DBSCAN-based clustering framework designed to identify recurring operational scenarios in container terminals. This is performed by jointly analysing spatiotemporal truck arrival patterns and container handling behaviours. The algorithm extends traditional density-based clustering by incorporating multi-dimensional distance metrics that capture spatial proximity, temporal alignment and operational similarity between container movements. The application of the proposed approach to a real-world case study from an Italian container terminal demonstrates its ability to extract five recurrent operational scenarios, covering more than 90% of container movements, with a noise ratio of 9.7% and a High-Density Score (HDS) of 0.4253, indicating a good balance between cluster cohesion, coverage, and operational interpretability. The identified scenarios were further analysed and qualitatively validated through interactions with terminal planners and operational managers. Overall, the resulting clusters provide actionable insights to support robust TAS design, enabling the development of data-driven decision support tools that explicitly account for operational variability and enhance the resilience of terminal planning and management.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"10 ","pages":"Article 100148"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-echelon synchronized routing and technician assignment for offshore wind farm maintenance","authors":"Parisa Torabi,&nbsp;Ahmad Hemmati","doi":"10.1016/j.martra.2026.100149","DOIUrl":"10.1016/j.martra.2026.100149","url":null,"abstract":"<div><div>Maintaining offshore wind farms (OWFs) is essential to ensure stable power production, but poses significant logistical and operational challenges due to the harsh marine environment, making it complicated to transport technicians and equipment.</div><div>This study introduces the problem of finding the route and assignment of technicians minimizing the total time required to complete all scheduled maintenance tasks at OWFs under three concurrent assumptions: (1) the compatibility of technicians’ skill-set and the maintenance task to be performed, (2) technicians’ routing in the form of drop-off and pick-up, and (3) synchronized two-echelon system, composed of accommodation vessel (AV) and crew transfer vessels (CTVs). Together, these assumptions create a realistic and operationally meaningful foundation for the problem. Skill-task compatibility ensures that only appropriately qualified technicians are assigned to each maintenance job, reflecting real workforce constraints. Modeling technicians’ movements as coordinated drop-off and pick-up routes cuts idle time of the vessels, as service times at turbines are typically much longer than the short travel times between them. Finally, the synchronized two-echelon system addresses the long commute distance from shore, enhancing operational efficiency.</div><div>The problem is modeled as a mixed integer linear program (MILP) model that accounts for various types of vessels and allows technicians to continue working independently after being dropped off. Finding feasible solutions to this problem is challenging, and solving it to optimality is extremely computationally complex. Thus, an adaptive matheuristic is designed to find high-quality solutions efficiently. Preliminary experiments on test instances based on Norwegian OWFs demonstrate that the proposed method yields robust and near-optimal solutions. We have also compared this method to a genetic algorithm that is adjusted to solve this specific problem and observe that, in most cases, the adaptive matheuristic reached better solutions with higher robustness. The impact of technician availability is also analyzed, showing that reducing crew size can significantly affect total operation time.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"10 ","pages":"Article 100149"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decision-support model of icebreaker prepositioning for northern sea route navigation: A weighted-demand approach","authors":"Chathumi Ayanthi Kavirathna ,&nbsp;Ryuichi Shibasaki","doi":"10.1016/j.martra.2025.100147","DOIUrl":"10.1016/j.martra.2025.100147","url":null,"abstract":"<div><div>The Northern Sea Route (NSR) has gained prominence as an international maritime corridor due to the retreat of Arctic sea ice, offering significant distance and time savings, particularly for shipping between Asia and Europe compared to conventional maritime routes. However, navigating the NSR involves considerable risks due to severe ice conditions along its navigation paths, making efficient icebreaking services critical for safe and reliable operations. The demand for icebreakers fluctuates spatially and temporally, depending on sea ice conditions and the ice class of cargo vessels. Additionally, icebreaking costs constitute a substantial portion of the overall voyage costs along the NSR. Strategically prepositioning icebreakers at optimum locations can help reduce these costs, enabling cargo vessels to request services more efficiently and minimize response times. This study focuses on the preparation stage of icebreaking services and introduces a weighted-demand response model to determine the optimum prepositioning of icebreakers before serving cargo vessels. The model considers expected vessel movements, navigation paths, and prevailing ice conditions. Eight Russian seaports are evaluated as potential prepositioning locations, and six ice classes—IC, IB, IA, IA-super, PC6, and PC5 are considered. The findings reveal that the optimum prepositioning locations and their priorities vary monthly in response to changing ice conditions, the composition of ice-class vessels, and their navigation directions. Moreover, Pevek Port consistently emerged as the highest-priority prepositioning location in most months. This study highlights the operational and policy implications of optimizing icebreaking services to reduce operating costs and improve the competitiveness of the NSR as an international maritime corridor.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"10 ","pages":"Article 100147"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the barriers to autonomous shipping","authors":"Sarah Marie Malmquist,&nbsp;Ziaul Haque Munim","doi":"10.1016/j.martra.2025.100135","DOIUrl":"10.1016/j.martra.2025.100135","url":null,"abstract":"<div><div>The adoption of Maritime Autonomous Surface Ships (MASS) in commercial shipping presents significant challenges despite rapid technological advancements. This study explores the barriers to the commercial adoption of MASS. Through a systematic literature review, 60 barriers were identified and categorized into four themes: (1) human factors, (2) data and risk management, (3) technology and connectivity, and (4) operations and policy. To reveal the most critical barriers, the importance-improvement (A-B) analysis was conducted utilizing data collected from maritime stakeholders. The analysis revealed that the most critical barriers include the trustworthiness of autonomous technology, managing loss of autonomous control system, vulnerabilities to cyberattacks, and the complexities of regulatory compliance in system development and deployment. Future resources and investments should be directed towards addressing the most critical barriers identified in this study for ensuring the successful integration of MASS in commercial shipping.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100135"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge transfer Q-learning for vessel route planning using automatic identification system-derived expert trajectories","authors":"Hyunju Lee ,&nbsp;Kikun Park ,&nbsp;Hyerim Bae","doi":"10.1016/j.martra.2025.100142","DOIUrl":"10.1016/j.martra.2025.100142","url":null,"abstract":"<div><div>Traditional route recommendation systems optimize navigation paths using environmental variables such as weather and sea conditions, but often fail to account for real-world factors encountered by mariners. To address this gap, this study proposes a knowledge transfer Q-learning (KT-QL) algorithm, a reinforcement learning method built upon the Q-learning framework. The proposed KT-QL algorithm integrates expert trajectory knowledge derived from Automatic Identification System data into the Q-learning process, enabling the agent to combine trial-and-error exploration with data-driven guidance. Experimental results show that KT-QL reduces Hausdorff distances by approximately 39 % compared with conventional reinforcement learning and traditional search methods, and enhances fuel consumption prediction accuracy by approximately 2 %. These findings highlight the potential of KT-QL to enhance maritime operational efficiency, safety, and environmental sustainability.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100142"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Creating a digital twin platform for maritime decarbonization by AI-assisted CII measure prediction: A case of chemical tanker","authors":"Hadi Taghavifar","doi":"10.1016/j.martra.2025.100141","DOIUrl":"10.1016/j.martra.2025.100141","url":null,"abstract":"<div><div>Carbon emission reduction has been the focus of the International Maritime Organization (IMO), and restrictive mandates are considered by the Marine Environment Protection Committee (MEPC). The new guidelines consider carbon dioxide (CO₂) emissions based on the propulsion system efficiency, distance, and dead weight, which are called the carbon intensity indicator (CII). In this research, this factor was calculated based on the large available data from a chemical tanker ship to analyze the ship rating using artificial intelligence techniques. The available data, consisting of global positioning system (GPS) location, wind speed and direction, draft and trim, engine power and speed, and vessel speed, are used for the CII prediction by the artificial neural network (ANN) modeling. Two types of ANN are considered for modeling: multilayer feedforward with two hidden layers, called deep neural networks (DNN), and generalized regression neural networks (GRNN). The attained, required, and referenced CII are calculated, and the system rating is determined and compared with the predicted CII. The best performance of the DNN is achieved with 15 neurons in the first and second hidden layers. The performance of the two types of ANN is robust and close to each other. However, the GRNN has slightly better predictive efficiency, considering the faster convergence and setup configuration complexity. The GRNN model shows a mean absolute error of 0.0928 with an unacceptable prediction ratio of 0.06 % and a coefficient of determination R² = 0.998, which can capture the CII metric values and trend in transient mode robustly.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100141"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the value of vessel information sharing","authors":"Pim Willem Antoon van Leeuwen,&nbsp;Rommert Dekker","doi":"10.1016/j.martra.2025.100140","DOIUrl":"10.1016/j.martra.2025.100140","url":null,"abstract":"<div><div>Efficient and timely vessel arrival planning is crucial for smooth operations in maritime transportation networks, ensuring optimal resource utilization and minimizing operational costs. When proforma schedules are disturbed by arrival deviations of vessels, waiting time and unnecessary fuel consumption become problems that shipping lines are faced with. Using a simulation model of a single-berth terminal, we test speed selection strategies for vessels that aim to minimize fuel, sailing, and waiting costs under varying availability of information. In different scenarios, we find optimality gaps ranging from 0.1% to 19.6% and show that knowing and communicating service end time to the vessel calling next could be valuable to integrated shipping lines and terminals.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100140"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maritime inventory routing with an application to fish feed distribution","authors":"Anders Bjelland ,&nbsp;Aksel Borgen ,&nbsp;Sjur Wold ,&nbsp;Kjetil Fagerholt ,&nbsp;Dimitri J. Papageorgiou ,&nbsp;Kristian Thun ,&nbsp;Simen Tung Vadseth","doi":"10.1016/j.martra.2025.100139","DOIUrl":"10.1016/j.martra.2025.100139","url":null,"abstract":"<div><div>This paper studies a maritime inventory routing problem (MIRP) faced by fish feed suppliers responsible for distributing different types of fish feed from one or several production facilities to a number of fish farms located at sea with a given heterogeneous fleet of specialized vessels. The feed supplier needs to maintain sufficient inventory levels at the farms at all times while minimizing the distribution costs. We propose a discrete-time mixed-integer programming (MIP) model for the fish feed MIRP. Since a commercial MIP-solver can only solve small problem instances, we also propose a matheuristic for solving real-life instances. The matheuristic employs a memetic algorithm, a metaheuristic combining a genetic algorithm with local search to decide how to route the vessels, coupled with a linear program for assigning quantities along the vessel routes. We perform a computational study on a number of realistic test instances generated using data from one of Norway’s largest fish feed suppliers. We show that the matheuristic produces reasonable solutions where the commercial MIP-solver fails, and as such can provide valuable decision support.</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100139"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A composite port resilience index focused on climate-related hazards: Results from Greek ports’ living-labs","authors":"Amalia Polydoropoulou ,&nbsp;Adonis Velegrakis ,&nbsp;Georgios Papaioannou ,&nbsp;Ioannis Karakikes ,&nbsp;Efstathios Bouhouras ,&nbsp;Helen Thanopoulou ,&nbsp;Dimitrios Chatzistratis ,&nbsp;Isavela Monioudi ,&nbsp;Konstantinos Moschopoulos ,&nbsp;Antonis Chatzipavlis","doi":"10.1016/j.martra.2025.100136","DOIUrl":"10.1016/j.martra.2025.100136","url":null,"abstract":"<div><div>This paper develops a composite Port Resilience Index (PRI) to address the specific vulnerabilities and operational challenges of Greek ports in respect to climate-related hazards. Based on stakeholder engagement from Living Labs in three key ports (Chios, Volos, and Heraklion), the study identifies and quantifies the impacts of climate-related hazards using a structured Multi-Criteria Decision Analysis (MCDA) framework. Specifically, the Analytic Hierarchy Process (AHP) is used to elicit expert judgments and prioritize resilience criteria across five impact areas: Infrastructure, Operational and Supply Chain, Digital, Socioeconomic and Environmental, and Governance and Compliance Resilience. Nineteen indicators, spanning physical infrastructure, operational reliability, digital readiness, and socioeconomic factors, are evaluated to construct a composite PRI, enabling a transparent and stakeholder-informed benchmarking process. The results reveal significant variation in resilience levels, with Volos exhibiting the highest PRI (0.643) and Chios the lowest (0.217), thereby highlighting port-specific adaptation needs. Conducting a sensitivity analysis we validated the robustness of the PRI construction methodology across various weighting scenarios. The key contributions of this study are: (i) the development of a replicable, data-driven PRI model; (ii) the integration of local stakeholder input via Living Labs; and (iii) the innovative application of AHP to climate resilience planning in the port industry. Moreover, while focused on Greek ports, the framework offers a replicable model that can be adapted to other regions facing similar climate challenges. Ultimately, the PRI serves as both a diagnostic and strategic tool to guide policy, investment, and disaster preparedness in ports</div></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"9 ","pages":"Article 100136"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}