Search Results (229)

It is challenging to manoeuvre large deck cargo barges within the confined, congested port waters, especially when berthing and unberthing at a U-shaped basin. To investigate the safety operation of those ships under these complex circumstances, the research employs an integrated methodology to enhance safety. Ship manoeuvring simulations were first conducted to determine the critical environmental limits (including wind, current, and wave thresholds) under which safe operations are feasible. Subsequently, for safe mooring, Computational Fluid Dynamics (CFD) simulations were applied to analyse the hydrodynamic forces acting on the barge while berthed. These CFD results were crucial for determining the optimal mooring configuration (number, type, and arrangement of lines) required to sustain the environmental loads. The combined insights from manoeuvring simulations and CFD analysis provide a comprehensive framework for port planners and mariners, which will substantially improve the operational safety of large deck cargo barges utilising U-shaped berths in busy and spatially constrained port areas. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

Airborne dust emissions from bulk cargo handling in port terminals can degrade local air quality, but traditional dispersion models are often too slow or coarse to support rapid operational decisions. There is thus a pressing need for efficient tools that retain the spatial detail of CFD while enabling near-real-time scenario evaluation. In this work, we develop and test a hybrid framework that couples an RANS-based CFD model of dust dispersion with a neural network surrogate to rapidly predict exposure patterns for a bulk terminal under variable wind and operational conditions. The ML surrogate model, based on a decoder-style Multilayer Perceptron (MLP) architecture, processes two-dimensional slices of dispersion fields across particle diameter classes, enabling predictions in milliseconds with an acceleration factor of approximately $8\times {10}^6$ over traditional CFD while preserving high fidelity, as validated by performance metrics such as the F₁ score and precision values exceeding $0.8$ and $0.76$, respectively. This approach not only addresses computational inefficiencies but also lays the groundwork for real-time air-quality monitoring and sustainable urban planning, potentially integrating with digital twins fed by live weather data. Full article

The shipping service industry plays a pivotal role in enhancing port competitiveness and fostering urban economic growth, yet limited studies systematically integrate its spatial temporal dynamics with the processes driving port–city synergy. This study constructs a three-dimensional analytical framework encompassing port operations, urban economic development, and shipping service industry agglomeration. Using data from 13 port cities in Jiangsu Province (2015–2023), we apply the entropy weight method, coupling coordination degree model, relative development model, and panel Tobit regression to evaluate interaction intensity, coordination patterns, and influencing factors. Results reveal a clear spatial gradient in coupling coordination, higher in southern Jiangsu and lower in the north, driven by disparities in economic foundations, port capacities, and service industry structures. In most cities, port operations and urban economies lag behind shipping service industry agglomeration, reflecting the predominance of low- and mid-end services. Port construction level, cargo and container throughput, economic development, openness, fixed asset investment, and population density significantly promote coordination, whereas R&D capacity shows no significant effect. The findings advance understanding of port–city service interlinkages and provide targeted policy recommendations for differentiated regional development, infrastructure enhancement, and upgrading toward high-end shipping services, with implications for maritime regions worldwide. Full article

Ensuring worker safety compliance and secure cargo transportation in complex port environments is critical for modern logistics hubs. However, conventional supervision methods, including manual inspection and passive video monitoring, suffer from limited coverage, poor real-time responsiveness, and low robustness under frequent occlusion, scale variation, and cross-camera transitions, leading to unstable target association and missed risk events. To address these challenges, this paper proposes CSPC-BRS, a real-time multi-object detection and tracking framework for open-channel port scenarios. CSPC (Coordinated Spatial Perception Cascade) enhances the YOLOv8 backbone by integrating CASAM, SPPELAN-DW, and CACC modules to improve feature representation under cluttered backgrounds and degraded visual conditions. Meanwhile, BRS (Bounding Box Reduction Strategy) mitigates scale distortion during tracking, and a Multi-Dimensional Re-identification Scoring (MDRS) mechanism fuses six perceptual features—color, texture, shape, motion, size, and time—to achieve stable cross-camera identity consistency. Experimental results demonstrate that CSPC-BRS outperforms the YOLOv8-n baseline by improving the mAP@0.5:0.95 by 9.6% while achieving a real-time speed of 132.63 FPS. Furthermore, in practical deployment, it reduces the false capture rate by an average of 59.7% compared to the YOLOv8 + Bot-SORT tracker. These results confirm that CSPC-BRS effectively balances detection accuracy and computational efficiency, providing a practical and deployable solution for intelligent safety monitoring in complex industrial logistics environments. Full article

With the rapid development of remote sensing technology and deep learning, the port ship detection based on a single-stage algorithm has achieved remarkable results in optical imagery. However, most of the existing methods are designed and verified in specific scenes, such as fixed viewing angle, uniform background, or open sea, which makes it difficult to deal with the problem of ship detection in complex environments, such as cloud occlusion, wave fluctuation, complex buildings in the harbor, and multi-ship aggregation. To this end, ES-YOLO framework is proposed to solve the limitations of ship detection. A novel edge perception channel, Spatial Attention Mechanism (EACSA), is proposed to enhance the extraction of edge information and improve the ability to capture feature details. A lightweight spatial–channel decoupled down-sampling module (LSCD) is designed to replace the down-sampling structure of the original network and reduce the complexity of the down-sampling stage. A new hierarchical scale structure is designed to balance the detection effect of different scale differences. In this paper, a remote sensing ship dataset, TJShip, is constructed based on Gaofen-2 images, which covers multi-scale targets from small fishing boats to large cargo ships. The TJShip dataset was adopted as the data source, and the ES-YOLO model was employed to conduct ablation and comparison experiments. The results show that the introduction of EACSA attention mechanism, LSCD, and multi-scale structure improves the mAP of ship detection by 0.83%, 0.54%, and 1.06%, respectively, compared with the baseline model, also performing well in precision, recall and F1. Compared with Faster R-CNN, RetinaNet, YOLOv5, YOLOv7, and YOLOv8 methods, the results show that the ES-YOLO model improves the mAP by 46.87%, 8.14%, 1.85%, 1.75%, and 0.86%, respectively, under the same experimental conditions, which provides research ideas for ship detection. Full article

The shift to green ports is essential for meeting worldwide sustainability targets and lowering emissions related to maritime activities. This paper presents a comprehensive analysis of the Port of Bar in Montenegro and its prospects for transforming into a low-carbon sustainable port hub within the Adriatic region. By a mixed-method approach consisting of empirical data, theoretical modeling, expert interviews, and other relevant methodologies, the study designs a comprehensive roadmap for the port’s multi-phase green transition. The first phase (2026–2030) focuses on partial electrification of cargo handling equipment, installation of on-site photovoltaic systems, and modernization of the Port Community System (PCS) to improve efficiency and environmental monitoring. The second phase (2030–2038) includes full electrification of port operations, Onshore Power Supply (OPS) accessibility for vessels at berth, and full renewable resource adoption. Results indicate the measures can significantly reduce annual CO₂ emissions during the first phase, with a long-term potential to attain net-zero emissions. This transformation is in line with international regulations, European Union policies, as well as Montenegro’s national strategies and policies, positioning the Port of Bar as a regional model for green port development. Full article

Ports face critical security threats from hazardous cargo misdeclaration, which poses unique challenges due to its high concealment and catastrophic potential, as exemplified by the Beirut Port explosion. Traditional resilience assessment approaches relying on hazard state transition probabilities require abundant historical data or extensive domain expertise for probability elicitation, and static indicator-based assessment frameworks fail to capture the spatiotemporal evolution characteristics of disasters. To address these challenges, this study proposes a hybrid framework that leverages the Large Language Model (LLM)’s generalizable world knowledge for data augmentation while developing a Spatiotemporal Graph Neural Network (STGNN) to predict dynamic disaster propagation. Specifically, a multimodal LLM is employed to extract structured port state descriptions from temporally aligned disaster data and infer the states at undocumented time steps. With more disaster scenarios adapted from the real cases using the LLM, a STGNN is trained to learn the disaster evolution dynamics and make efficient real-time inference for resilience assessment and intervention strategy evaluation. Validation on Tianjin and Beirut Port incidents demonstrates that the framework accurately predicts disaster propagation pathways and identifies critical intervention priorities. It also reveals that topology-based intervention strategies substantially accelerate recovery, while adverse environmental conditions significantly amplify cumulative functional loss. This study represents an advancement toward AI-driven resilience modeling, offering port operators and regulators an adaptable, scalable decision support tool for intelligent safety governance. Full article

As a globally leading port in cargo throughput, the operational efficiency of Ningbo-Zhoushan Port’s container transport system significantly impacts regional economic development. To deeply analyze the spatio-temporal patterns of container truck transportation, this study utilized GPS trajectory data from over 5000 container trucks between 27 October and 26 November 2024. Employing the DBSCAN algorithm and Prophet time series model, it identified transportation hotspots and abnormal trajectories while decomposing the temporal characteristics of collection and distribution operations. Findings reveal: Spatially, container truck movements exhibit a pronounced “core-periphery” structure, with East China as the primary hinterland (285,000 trips), followed by South and Central China (approximately 2000 trips each), while other regions recorded fewer than 700 trips. At the provincial level, Zhejiang, Jiangsu, Anhui, and Jiangxi constitute the high-frequency tier (97.48%), central provinces like Hubei and Hunan belong to the medium-frequency tier (1.99%), while other provinces fall into the low-frequency tier (0.53%). At the city level, core cities like Ningbo and Jinhua accounted for 92.20% of the total, with transport frequency showing a clear decline with increasing distance. Temporally, the daily peak period occurred between 6:00 and 17:00, while the off-peak period was 9:00–14:00. Weekly variations showed a continuous rise from Monday to Thursday, peaking on Thursday before gradually declining. This study employs big data analysis to validate the influence of economic geography on port hinterland patterns while revealing spatiotemporal dynamics inaccessible through traditional research methods. Findings provide empirical evidence for the “distance decay” theory. The research holds significant theoretical and practical value for optimizing port collection and distribution systems and promoting multimodal transport coordination, offering a scientific basis for smart port transformation. Full article

Shipping is a major source of carbon emissions and faces an urgent need for decarbonization. Research on vessel carbon emissions not only characterizes regional emission patterns but also provides critical evidence for targeted mitigation policies and optimized maritime management. This study quantifies vessel carbon emissions in the Port of New York and New Jersey from February to November 2023 using Automatic Identification System (AIS) data combined with the STEAM model. An activity-weighted spatial allocation method was applied to distribute emissions across 100 m × 100 m grids. Emission characteristics were analyzed across four dimensions: vessel type, operational state, temporal variation, and spatial distribution. Results show that total emissions during the study period reached approximately 136,701.8 t, with container ships contributing 62.3% of the total. Berthing operations were identified as the dominant emission source, accounting for 73.4% of total emissions, followed by tugboats and cargo vessels. Temporally, emissions peaked in October (10.8%) and were lowest in February (8.8%), reflecting variations in trade intensity and seasonal weather conditions. Spatially, emissions exhibited strong clustering around terminal berths. A sensitivity analysis was performed to assess the robustness of the emission estimates. When the load factor (LF) varied by ±10%, total emissions changed by only ±1.85%, indicating that the results are highly stable and robust. This limited variation arises from the dominance of berthing operations with relatively steady auxiliary loads and the application of the constraint LF ≤ 1, which prevents unrealistic overloading. These findings offer indicative insights that can inform port-level emission management and serve as a reference for future low-carbon policy development. Full article

This study focuses on an integrated three-level multi-port liner ship vessel routing and scheduling optimization problem. Specifically, the three-level multi-port network consists of hub ports, feeder ports, and cargo source points, which provide the demands’ loading/unloading at each port. Considering vessel-specific constraints such as speed, capacity, and cost, we formulate the multi-port liner ship routing and scheduling optimization problem as a mixed integer linear programming model with the objective of minimizing total voyage cost and operating time. First, we employ machine learning models to forecast the short-term demand at different ports as the input. There are multiple feasible routes generated and allowed to be elected. Second, to ensure both computational efficiency and solution quality, we devise and compare genetic algorithm (GA), simulated annealing (SA), Gurobi and the branch-and-price (B&P) algorithm to optimize scheduling plans. Experimental results demonstrate that the proposed predict-then-optimization framework effectively addresses the complexity of multi-port scheduling and routing problems, achieving a reduction in total transportation cost by 0.81% to 8.08% and a decrease in computation time by 16.86% to 24.7% compared to baseline methods, particularly with the SA + B&P hybrid approach. This leads to overall efficiency and cost-saving ocean vessel operations. Full article

Maritime transport plays a pivotal role in global trade, where efficiency and accuracy in port operations are crucial. Among the various tasks carried out in ports, container code recognition is essential for tracking and handling cargo. Manual inspections of container codes are becoming increasingly impractical, as they induce delays and raise the risk of human error. To address these issues, this work proposes a hybrid Optical Character Recognition system that integrates YOLOv7 for text detection with the transformer-based TrOCR for recognition of the container codes, enabling accurate and efficient automated recognition. This design addresses the real-world challenges, such as varying light, distortions, and multi-orientation of container codes. To evaluate the system, we conducted a comprehensive evaluation on datasets that simulate the conditions found in port environments. The results demonstrate that the proposed hybrid model delivers significant improvements in detection and recognition accuracy and robustness compared to traditional OCR methods. In particular, the reliability in recognizing multi-oriented codes marks a notable advancement compared to existing solutions. Overall, this study presents an approach to automating container code recognition, contributing to the efficiency and modernization of port operations, with the potential to streamline port operations, reduce human error, and enhance the overall logistics workflow. Full article

The increasing need to optimize efficiency in port container terminals has led to the transition of operations from manual to automated or semi-automated processes. Automation involves integrating or gradually adopting digital technologies and equipment that reduce human intervention, enhance productivity, safety and sustainability. This study investigates the impact of automation on port efficiency through a comparative analysis of 20 container ports in the wider Mediterranean region, using a two-stage modeling approach. In the first stage, Data Envelopment Analysis (DEA) is applied under constant and variable returns to scale to estimate port efficiency using infrastructure, equipment, and container throughput data. The second stage employs Tobit regression to assess the effect of automated operations or systems on port efficiency, including variables such as the automation index, TEUs per employee, TEUs per ship (call) and revenue. A key contribution of this study is the development of a methodological framework for qualitatively classifying and evaluating these ports based on their level of automation, the introduction of digital technologies or equipment, and investments in new technologies. The results indicate that automation alone does not necessarily lead to higher efficiency unless it is effectively integrated into operations accompanied by adequate staff training and supported by gradual investment strategies. By contrast, cargo intensity (TEUs per call), highlights the importance of vessel size and cargo concentration in improving port performance. Full article

The port of Gaženica, managed by the Port Authority of Zadar, is open to public traffic of special economic interest to the Republic of Croatia. Situated outside Zadar’s city centre, with convenient access to the airport and A1 highway, this port presents significant opportunities for Zadar County’s economic growth. While also serving as a cargo and fishing port, as the second-largest passenger port in Croatia, the port of Gaženica prioritises the development of cruise ship traffic. The expansion of intermodal traffic is being facilitated through the development of a multipurpose terminal to accommodate general, roll-on/roll-off, and containerised cargo (full and empty containers). The rising number of passenger ships—particularly cruise ships—along with the increasing passenger, vehicle, and cargo traffic, poses a significant risk of pollution due to dust, noise, greenhouse gases, and other pollutants. Considering these risks, the use of alternative energy sources, decarbonisation of maritime transport, the separation of waste by type, and the proper handling and disposal of ship waste are of utmost importance. The aim of this study is to present and analyse the green transition process of the port of Gaženica through the results that have been achieved or are yet to be achieved through the implementation of green projects by the Port Authority of Zadar. For this purpose, a mixed-methods approach combining project analysis and the qualitative analysis of emissions data is used. It is important to highlight that the method of interviews with relevant representatives of institutions involved in the project was also used to gain insight into financial and infrastructural challenges, the accessibility of certain data, and potential improvements in implementation. The research results indicate that the port of Gaženica has completed four green projects, while another four are currently being implemented, with their completion expected by 2026. The research concludes that it is necessary to strengthen environmental awareness regarding proper waste disposal among all stakeholders in maritime transport, including the local community, businesses, and local authorities. The results demonstrate a need to focus on certification with the aim of strengthening the green transition process through involvement in the EcoPorts and Green Award certification schemes. It is also necessary to actively improve the public availability of data from the base station in the port of Gaženica to inform the public about environmental impacts in real time (24/7) while facilitating data collection for statistical reporting purposes. Full article

As maritime transport continues to grow, the volume and complexity of waste generated by ships, such as garbage, sewage, and oily residues, requires the establishment of effective, accessible and well-regulated collection systems in ports. Ensuring effective waste management remains a major challenge across the European Union, as differences in national implementation and charging systems continue to undermine the sustainability of port reception facilities. Directive (EU) 2019/883 on port reception facilities (PRF Directive) was introduced to harmonise regulatory standards, ensure adequate infrastructure, and remove barriers to proper waste management. This paper analyses the transposition and implementation of the PRF Directive in selected EU countries, focusing on the differences in cost recovery systems (CRS) applied in ports. A comparative analysis of charging models and waste management plans for ports is carried out, including an in-depth study of the leading European ports with the highest reported waste volumes. A nine-criteria evaluation framework was developed through a stakeholder focus group involving port authorities, concessionaires, shipping companies, and the Harbour Master’s Office, and was applied using the multi-criteria TOPSIS decision methodology, complemented by sensitivity analyses and adjustments for different port types (cargo, passenger, fisheries, marinas). The results show that the best-performing models achieved C* values between 0.514 and 0.529, confirming the robustness of the evaluation framework. Overall, the findings indicate that the optimal charging model is context-dependent, with No-Special-Fee systems without special charges favoured in passenger and leisure ports, and Prepaid + Reimbursement models more suitable for cargo and fishing ports. The paper concludes with policy recommendations aimed at increasing transparency, ensuring consistent reporting, and aligning CRS models more closely with EU environmental objectives. Full article