Search Results (26)

Knowledge about vessel activity in port areas and around major industrial zones provides insights into economic trends, supports decision-making for shipping and port operators, and contributes to maritime safety. Vessel data from terrestrial receivers of the Automatic Identification System (AIS) have become increasingly openly available, and we demonstrate that such data can be used to infer port activities at high resolution and with precision comparable to official statistics. We analyze open-access AIS data from a three-month period in 2024 for Tokyo Bay, located in Japan’s most densely populated urban region. Accounting for uneven data coverage, we reconstruct vessel activity in Tokyo Bay at ~30 m resolution and identify 161 active berths across seven major port areas in the bay. During the analysis period, we find an average of $35\pm {17}_{\mathrm{stat}}$ vessels moving within the bay at any given time, and $293\pm {22}_{\mathrm{stat}}+{65}_{\mathrm{syst}}-{10}_{\mathrm{syst}}$ vessels entering or leaving the bay daily, with an average gross tonnage of ${\mathrm{11,860}}_{-50}^{+280}$. These figures indicate an accelerating long-term trend toward fewer but larger vessels in Tokyo Bay’s commercial traffic. Furthermore, we find that in dense urban environments, radio shadows in vessel AIS data can reveal the precise locations of inherently passive receiver stations. 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

Port berthing sites are regions of high interest for monitoring and optimizing port operations. Data sourced from the Automatic Identification System (AIS) can be superimposed on berths, enabling their real-time monitoring and revealing long-term utilization patterns. Ultimately, insights from multiple berths can uncover bottlenecks, and lead to the optimization of the underlying supply chain of the port and beyond. However, publicly available documentation of port berths, even when available, is frequently incomplete—e.g., there may be missing berths or inaccuracies such as incorrect boundary boxes—necessitating a more robust, data-driven approach to port berth localization. In this context, we propose an unsupervised spatial modeling method that leverages AIS data clustering and hyperparameter optimization to localize berthing sites. Trained on one month of freely available AIS data and evaluated across ports of varying sizes, our models significantly outperform competing methods, achieving a mean Bhattacharyya distance of 0.85 when comparing Gaussian Mixture Models trained on separate data splits, compared to 13.56 for the best existing method. Qualitative comparison with satellite images and existing berth labels further supports the superiority of our method, revealing more precise berth boundaries and improved spatial resolution across diverse port environments. Full article

This study presents an integrated assessment of anchorage-related emissions and air quality impacts in the Panama Canal region through Automatic Identification System (AIS) data, bottom-up emission estimation, and atmospheric dispersion modeling. One year of terrestrial AIS observations (July 2024–June 2025) captured 4641 vessels with highly variable waiting times: mean 15.0 h, median 4.9 h, with maximum episodes exceeding 1000 h. Annual emissions totaled 1,390,000 tons of CO₂, 20,500 tons of NO_x, 4250 tons of SO₂, 656 tons of PM₁₀, and 603 tons of PM_2.5, with anchorage activities contributing 497,000 tons of CO₂, 7010 tons of NO_x, 1520 tons of SO₂, 232 tons of PM₁₀, and 214 tons of PM_2.5. Despite the main engines being shut down during anchorage, these activities consistently accounted for 34–36% of the total emissions across all pollutants. High-resolution emission mapping revealed hotspots concentrated in anchorage zones, port berths, and canal approaches. Dispersion simulations revealed strong meteorological control: northwesterly flows transported emissions offshore, sea–land breezes produced afternoon fumigation peaks affecting Panama City, and southerly winds generated widespread onshore impacts. These findings demonstrate that anchorage operations constitute a major source of shipping-related pollution, highlighting the need for operational efficiency improvements and meteorologically informed mitigation strategies. Full article

This study investigates the impact of reducing waiting times for port berth on improving the Carbon Intensity Indicator (CII) ratings of Korean-flagged container ships. As the International Maritime Organization (IMO)’s CII regulation mandates corrective actions for poorly rated ships for Greenhouse Gas (GHG) reduction in international shipping, the analysis focuses on container ships with projected D or E ratings by 2035. Using Automatic Identification System (AIS) data from ships, this study identifies annual waiting times and simulates changes in CII ratings under scenarios of reduced waiting times (30%, 50%, 70%, and 100%). The relationship between ship speed and fuel consumption was established by analyzing the recent literature, and the CII improvement was evaluated based on IMO Data Collection System (DCS) 2022 data. The results show that a 30% reduction in waiting time can lower CO₂ emissions by 12.18% and improve the CII rating by one or two levels for approximately half of the sample ships. However, a 50% reduction or more is required to maintain improved ratings beyond 2030. The findings highlight the significance of just-in-time (JIT) practices in minimizing latency and enhancing regulatory compliance. The policy recommendations advocate for prioritizing port call optimization and recommend the adoption of JIT as a measure to achieve the IMO’s GHG reduction targets. Full article

This study conducts a high-resolution forensic evaluation of stern structural damage resulting from a tugboat collision during berthing, integrating real-world measurement data with calibrated nonlinear finite element analysis. Based on field-acquired deformation geometry and residual dent profiles at Frame 76, five distinct collision scenarios varying in impact orientation, contact area, and load path were simulated using shell-based nonlinear plastic analysis. Particular attention is given to comparing the plastic equivalent strain (PEEQ), von-Mises stress fields, and residual deformation contours at Point A—the critical zone identified from damage surveys. Among the five cases, Case-2, defined by a vertically eccentric external impact, demonstrated the highest plastic strain intensity (PEEQ > 2.0%), the sharpest post-yield drops in stiffness, and the closest match to the residual dent profile observed in the actual structure. The integrated correlation between field damage and some of the results (strain, stress, and deformed shape) enabled clear identification of the most probable accident mechanism with engineering accuracy. This study proposes a validated, measurement-calibrated nonlinear finite element analysis framework to diagnose stern damage from tugboat collisions, enhancing repair decision-making and structural safety assessment. Such a calibrated forensic strategy enhances the reliability of structural safety predictions in marine collision incidents and supports eco-friendly rescue engineering by minimizing unnecessary structural renewal through precise damage localization. The proposed approach establishes a new benchmark for scenario-driven collision assessment, particularly relevant to sustainable, automation-compatible, and damage-tolerant ship design practices. Full article

Accurate ship berthing behavior prediction (BBP) is essential for enabling collision warnings and support decision-making. Existing methods based on Automatic Identification System (AIS) data perform well in the task of ship trajectory prediction over long time-series and large scales, but struggle with addressing the fine-grained and highly dynamic changes in berthing scenarios. Therefore, the accuracy of BBP remains a crucial challenge. In this paper, a novel BBP method based on Light Detection and Ranging (LiDAR) data is proposed. To test its feasibility, a comprehensive dataset is established by conducting on-site collection of berthing data at Dalian Port (China) using a shore-based LiDAR system. This dataset comprises equal-interval data from 77 berthing activities involving three large ships. In order to find a straightforward architecture to provide good performance on our dataset, a cascading network model combining convolutional neural network (CNN), a bi-directional gated recurrent unit (BiGRU) and bi-directional long short-term memory (BiLSTM) are developed to serve as the baseline. Experimental results demonstrate that the baseline outperformed other commonly used prediction models and their combinations in terms of prediction accuracy. In summary, our research findings help overcome the limitations of AIS data in berthing scenarios and provide a foundation for predicting complete berthing status, therefore offering practical insights for safer, more efficient, and automated management in smart port systems. Full article

Periodic change in reservoir water level will have a significant impact on berthing position, and the impact caused by irregular operation during berthing will cause damage to wharf pile foundations. However, most of the existing monitoring methods adopt irregular methods, so it is difficult to accurately identify and analyze the damage causes. Taking a high-piled wharf in the Three Gorges Reservoir area as an example, the uncertainty of reservoir water level change is quantitatively analyzed. By establishing a simplified parametric wharf calculation model, the data set of an inversion model of pile of a high-piled wharf under ship impact is obtained, and the inversion analysis of pile damage of a high-piled wharf under ship pile is carried out based on the artificial neural network model. The results show that the inversion model can accurately and efficiently identify the intensity of ship impact, and a low water level is better than a high water level in the identification of impact position. In this paper, the behavior of wharf structure before and after damage is analyzed symmetrically under the action of damage inducement. In summary, the inversion analysis method can basically meet the requirements of inversion identification of pile foundation damage of a high-pile wharf in a backwater fluctuation area under ship impact. Full article

Efficient logistics is a key factor in the competitiveness of seaports, especially in regions such as the Baltic Sea, where ports play important roles as hubs in the European Union’s Trans-European transport network (TEN-T). However, there are a lack of comprehensive studies focusing on the logistics efficiency of Baltic Sea ports, especially those integrating technical and technological factors. This study aimed to assess changes in the logistics efficiency of 15 major ports in the Baltic Sea region between 2019 and 2023, taking into account the technological and infrastructure-related elements that influence port performance. The model developed by the authors integrates the nearest neighbour method for cluster identification, data envelopment analysis using the Banker, Charnes, and Cooper (DEA-BCC) model to assess the overall technical, pure technical, and scale logistics efficiency, and spatial autocorrelation analysis to explore spatial interactions. For the DEA-BCC model, constraints were defined for each port based on inputs (number and length of berths) and outputs (cargo and container volumes for 2019–2023). The spatial autocorrelation analysis examined the relationships among the Baltic Sea ports, container volumes, and logistic efficiency values derived from the DEA model. Recognizing the sensitivity of the weight matrix in previous studies, this paper introduced an enhanced two-factor weighting matrix that incorporated geographical distance and the port connectivity index, calculated by the United Nations Conference on Trade and Development (UNCTAD). The statistical reliability of the results was validated using z-scores and p-values. The results showed that the overall technical efficiency of the ports analysed during the period considered was 47.2%, the pure technical efficiency was 61.0%, and the average scale efficiency was around 76%, indicating that diminishing returns to scale dominated. The spatial analysis showed a strong correlation between port connectivity and efficiency, indicating that well-connected ports, such as Gdańsk and Gdynia, had a higher efficiency. The findings make a significant contribution to the understanding of the logistics efficiency of Baltic Sea ports and highlights the importance of regional cooperation, infrastructure improvements, and better connectivity strategies to improve the overall efficiency of seaports in the region. Full article

Intelligent ships are a key focus for the future development of maritime transportation, relying on efficient decision-making and autonomous control within complex environments. To enhance the perception, prediction, and decision-making capabilities of these ships, the present study proposes a novel approach for constructing a time-series knowledge graph, utilizing real-time Automatic Identification System (AIS) data analyzed via a sliding window technique. By integrating advanced technologies such as knowledge extraction, representation learning, and semantic fusion, both static and dynamic navigational data are systematically unified within the knowledge graph. The study specifically targets the extraction and modeling of critical events, including variations in ship speed, course changes, vessel encounters, and port entries and exits. To evaluate the urgency of encounters, mathematical algorithms are applied to the Distance to Closest Point of Approach (DCPA) and Time to Closest Point of Approach (TCPA) metrics. Furthermore, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is employed to identify suitable docking berths. Additionally, multi-source meteorological data are integrated with ship dynamic data, providing a more comprehensive representation of the maritime environment. The resulting knowledge system effectively combines ship attributes, navigational status, event relationships, and environmental factors, thereby offering a robust framework for supporting intelligent ship operations. Full article

Image-based ship monitoring technology has extensive applications, and is widely used in various aspects of port management, including illegal activity surveillance, vessel identification at entry and exit points, channel and berth management, unmanned vessel control, and incident warning and emergency response. However, most current ship identification technologies rely on a single information source, reducing detection accuracy in the complex and variable marine environment. To address this issue, this paper proposes a knowledge transfer-based ship identification system integrating three modules. The system enables synchronized monitoring of visible light coastal images, satellite cloud images, and infrared spectrum images, thereby mitigating problems such as low detection accuracy and poor adaptability of image recognition. Additionally, extensive supplementary experiments were conducted to evaluate the effectiveness of the preprocessing and data augmentation modules as well as the transfer learning module. The study also discusses the limitations of current deep learning-based ship monitoring models, particularly their poor adaptability to image recognition and inability to achieve all-weather, round-the-clock monitoring. Experimental results based on three ship monitoring datasets demonstrate that the proposed system, by integrating three distinct detection conditions, outperforms other models with an F1-score of 98.74%, approximately 10% higher than most existing ship detection systems. Full article

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system. Full article

Reducing waiting times is crucial for ports to be efficient and competitive. Important causes of waiting times are cascading interactions between realistic hydrodynamics, accessibility policies, vessel-priority rules, and detailed berth availability. The main challenges are determining the cause of waiting and finding rational solutions to reduce waiting time. In this study, we focus on the role of the design depth of a channel on the waiting times. We quantify the performance of channel depth for a representative fleet rather than the common approach of a single normative design vessel. The study relies on a mesoscale agent-based discrete-event model that can take processed Automatic Identification System and hydrodynamic data as its main input. The presented method’s validity is assessed by hindcasting one year of observed anchorage area laytimes for a liquid bulk terminal in the Port of Rotterdam. The hindcast demonstrates that the method predicts the causes of 73.4% of the non-excessive laytimes of vessels, thereby correctly modelling 60.7% of the vessels-of-call. Following a recent deepening of the access channel, cascading waiting times due to tidal restrictions were found to be limited. Nonetheless, the importance of our approach is demonstrated by testing alternative maintained bed level designs, revealing the method’s potential to support rational decision-making in coastal zones. Full article

Container terminals are at the center of global logistics, and are highly dependent on the schedule of vessels arriving. Conventional ETA records from ships, utilized for terminal berth planning, lack sufficient accuracy for effective plan implementation. Thus, there is a pressing need for improved ETA prediction methods. In this research, we propose a novel approach that leverages past voyage route patterns to predict the ETA of container vessels arriving at a container terminal at Busan New Port, South Korea. By modeling representative paths based on previous ports of call, the method employs real-time position and speed data from the Automatic Identification System (AIS) to predict vessel arrival times. By inputting AIS data into segmented representative routes, optimal parameters yielding minimal ETA errors for each vessel are determined. The algorithm’s performance evaluation during the modeling period demonstrates its effectiveness, achieving an average Mean Absolute Error (MAE) of approximately 3 h and 14 min. These results surpass the accuracy of existing ETA data, such as ETA in the Terminal Operating System and ETA in the AIS of a vessel, indicating the algorithm’s superiority in ETA estimation. Furthermore, the algorithm consistently outperforms the existing ETA benchmarks during the evaluation period, confirming its enhanced accuracy. Full article

Ship exhaust gas has become an essential source of air pollution in recent years. To assess the impact of ship exhaust gas on the atmospheric environment and human health, this paper studies the problem of ship exhaust gas diffusion in the port area. According to automatic identification system (AIS) data, ship exhaust gas is estimated based on the bottom-up method, and the result of emission calculation is entered into a Gaussian puff model to calculate the superposition of the diffusion of gaseous pollutants from multiple ships. In addition, the results of a case study of the diffusion of ship exhaust gas in the western area of Shenzhen Port in China show that the distribution of the NO₂ concentration in the studied area is not stable, the diffusion of exhaust gas from multiple ships mainly affects some areas near large ship berths at night, and there is a small impact on the whole study area. This lays a foundation for monitoring and treating the atmospheric environment in the port area. Full article