AIS 到货物、再到部门的映射

ABSTRACT This study examines the development of a machine-learning model to forecast weekly throughputs of dry bulk cargo in the short term based on automatic identification system (AIS) data. Specifically, the weekly amounts of iron ore exported from several major ports in Australia and Brazil in the latter half of 2019 are forecasted three weeks in advance using a long short-term memory model. We examine many variables extracted from AIS data, including the vessel position, speed, draught, and destination, as the input features of the model. Consequently, we develop a highly accurate forecasting model that uses four influential variables derived from AIS data, namely, vessel traffic around the target port and in the region, vessel traffic at major partner import ports, and vessel traffic at the target port during the past year. Finally, by forecasting the weekly port cargo throughputs in the first half of 2020, which was affected by the COVID-19 pandemic, the applicability of the model is confirmed, even for ports where the throughput fluctuates significantly. In particular, this study demonstrates that AIS data are beneficial not only as a real-time traffic database but also as a database containing various related explanatory variables, including historical vessel traffic.

Maritime cargo capacity serves as a critical indicator of port efficiency and regional economic impact, yet reliable data remain constrained by operational and commercial complexities. This study addresses this gap by leveraging maritime big data to compare traditional empirical methods with machine learning approaches, integrating multi-source datasets (ship inbound/outbound records, vessel archives, and AIS data). Results demonstrate that the K-nearest neighbors (KNN) algorithm achieves 88% predictive accuracy on validation data—a 19-percentage-point improvement over conventional methods (69%). While training accuracy reached 95%, anomalous vessel operations in validation samples reduced performance to 88%, revealing the model’s sensitivity to real-world variability and underscoring the need for enhanced data preprocessing. These findings highlight machine learning’s potential to refine cargo capacity estimation while emphasizing the importance of robust data quality frameworks for operational deployment.

Global maritime emissions, a 3% contributor to greenhouse gases, anticipate a surge of 90–130% by 2050. Regulatory challenges persist due to international governance gaps. Legislative strides, including the EU Emission Trading System, highlight global efforts. In the U.S., despite legislative commitment, consensus hurdles impede cross-regional carbon management. Prevailing top-down emissions estimation methods warrant scrutiny. This paper unveils U.S. maritime emissions intricacies, focusing on carbon accounting, transfer, and compensation for cargo and tanker vessels. Leveraging AIS data (2018–2022), an activity-based/bottom-up approach navigates emissions calculations, aiming to reshape understanding and foster strategic reductions. The study bridges gaps in U.S. maritime emission research, promising insights into transfer and compensation dynamics. By concentrating on high-impact vessel types, it contributes to emissions mitigation strategies, steering towards a sustainable U.S. maritime future.

The current article analyzes big data and makes predictions for the sea freight transport trends. It aims to support the management of sea transport, improve sea logistics services to predict trends in sea freight transport, support the management of sea transport, improve sea logistics services, and optimize the organization of transport services. The used data are data from the Automatic identification system (AIS data). The methods used are SQL queries and Excel functions for data extraction and calculations for data to receive results about seasonality and structural change indexes in five years (2019-2023). The study examines seasonality and idle ship indexes for the most commonly used ship types carrying cargo divided into three categories: cargo, tanker, and other types of ships. The results of calculations indicate a pronounced seasonality in the number of idle ships transiting through the Port of Varna, which appears to be directly proportional to the "idle ship" metric. Specifically, a decline in the idle ship index correlates with a decrease in the overall number of vessels. This relationship leads to an important inference: no unnecessary delays occur at the Port of Varna. Instead, any significant delays observed are primarily attributed to an increased number of ships during certain months. By understanding the cyclical patterns and supply-demand dynamics in the sea freight market, logistics providers can make more informed decisions and adapt their strategies accordingly. For the research period, other similar research is absent in the field of big data analysis in maritime traffic.

Maritime transportation is one of the most economically efficient methods of transporting large volumes of cargo and serves as a crucial vehicle for global trade, accounting for about 80% of worldwide commerce. In particular, since Korea is surrounded by sea on three sides, more than 90% of its import and export cargo heavily relies on maritime transportation. However, the COVID-19 pandemic that began in late 2019 has brought about changes in maritime traffic patterns due to global border controls and changes in economic activity. In particular, for Korea, which is highly dependent on maritime logistics, it is important to understand how the pandemic has affected maritime traffic density and the changes by vessel type. In this study, the changes in maritime traffic density in Korea during the first year of the pandemic, starting from March 11, 2020, are examined. Korea's sea area was divided into grid cells, and the length of ship trajectories within each grid cell was calculated to analyze changes in maritime traffic density. Maritime traffic density changes were analyzed for cargo ships, tanker ships, and passenger ships based on specific operating patterns, and traffic density within major ports was also examined. As a result, an increase in traffic density was observed for both cargo and tanker ships. The purpose of this study is to quantitatively analyze changes in maritime traffic density before and during the COVID-19 pandemic, thereby providing basic data for predicting maritime traffic patterns and developing response strategies in similar global crisis situations in the future.

The outbreak of COVID-19 has impacted the shipping industry while the extent of the impact is still not fully understood. To quantitatively investigate the relationship between pandemic-related factors and port operations, a panel regression analysis is conducted using data from three important Asian ports, Shenzhen, Hong Kong, and Singapore. Daily data from the Automatic Identification System (AIS), Oxford COVID-19 Government Response Tracker (OxCGRT) database, and port authorities from January 2020 to December 2021 are utilized. Local newly confirmed cases of ports tend to negatively impact cargo throughput, while worldwide newly confirmed cases outside of ports tend to positively impact cargo throughput. Overall, the policy implications are that ports with better control of COVID-19 reap the benefits of more cargo throughput. In addition, countermeasures against COVID-19 and other epidemics should be designed deliberately to minimize the side-effect on port operations and maritime transportation.

The maritime shipping network is the backbone of global trade. Data about the movement of cargo through this network comes in various forms, from ship-level Automatic Identification System (AIS) data, to aggregated bilateral trade volume statistics. Multiple network representations of the shipping system can be derived from any one data source, each of which has advantages and disadvantages. In this work, we examine data in the form of liner shipping service routes, a list of walks through the port-to-port network aggregated from individual shipping companies by a large shipping logistics database. This data is inherently sequential, in that each route represents a sequence of ports called upon by a cargo ship. Previous work has analyzed this data without taking full advantage of the sequential information. Our contribution is to develop a path-based methodology for analyzing liner shipping service route data, computing navigational trajectories through the network that both respect the directional information in the shipping routes and minimize the number of cargo transfers between routes, a desirable property in industry practice. We compare these paths with those computed using other network representations of the same data, finding that our approach results in paths that are longer in terms of both network and nautical distance. We further use these trajectories to re-analyze the role of a previously-identified structural core through the network, as well as to define and analyze a measure of betweenness centrality for nodes and edges.

Background: Improving container terminal efficiency requires a comprehensive understanding of the interactions between vessel, truck, and container operations, yet existing studies often analyzed these components separately. In Japanese container terminals, where digitalization initiatives are progressing, empirical evidence based on integrated operational data remains limited. Methods: This study empirically analyzes turnaround times for vessels, trucks, and containers at five major Japanese container terminals using a composite dataset that integrates terminal operating system data, automatic identification system data, and liner service information. Descriptive statistical analyses and regression models are applied to examine vessel berthing time, truck arrival patterns and turnaround time, container dwell time within terminals, and container round-trip time outside terminals. Results: The analysis reveals distinct temporal patterns in terminal operations, including systematic morning–afternoon asymmetries and differences across cargo flows. Truck turnaround times increase with vessel calls and vary by time of day, while container dwell times are strongly influenced by terminal policies such as free-time rules. Regression analyses indicate that turnaround times are primarily affected by terminal-controlled factors. Conclusions: These findings demonstrate the importance of synchronizing quayside and landside operations. The study contributes integrated empirical evidence to the port digitalization literature and provides actionable insights for enhancing container terminal efficiency.

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Strategic maritime chokepoints—especially the Red Sea/Suez system, the Strait of Malacca, and the Panama Canal—concentrate a disproportionate share of seaborne trade and therefore transmit shocks widely when disrupted. From 2000 to 2025 the literature evolved from network/port-system perspectives (e.g., port regionalization, connectivity indices) to event-based impact assessments (piracy, canal blockages, climate-driven restrictions), complemented by operational metrics such as schedule reliability and delay days. This article integrates those strands in a hybrid framework (AIS trajectory analysis + connectivity indices + event studies) and shows that shocks at chokepoints materially degrade reliability, extend transit times, and inflate costs, yet the magnitude and duration of those impacts depend on network structure (redundancy), governance, and timely operational responses.

This paper delves into the mechanisms of maritime congestion, employing an XGBoost-SHAP approach to analyze Automatic Identification System (AIS) data. Maritime shipping is pivotal to global supply chains, yet maritime traffic congestion impacts transportation efficiency and triggers environmental and economic concerns. The study harnesses the predictive prowess of the XGBoost model, complemented by the SHAP (SHapley Additive Explanations) method, to enhance model interpretability. By scrutinizing AIS data, the research uncovers key determinants of maritime congestion, including geographical location, vessel types, and navigational statuses. The findings reveal that geographical coordinates are the primary predictors of congestion, with specific vessel types and operational statuses exerting significant influence. These insights provide a scientific foundation and strategic recommendations for maritime management, aiming to bolster operational efficiency and inform policy-making, thereby enhancing maritime safety and environmental sustainability.

Roll-on/roll-off vessels (RO/RO vessels) are playing an increasingly critical role in international automobile transport, facilitating the efficient movement of vehicles and heavy machinery across continents. Despite this growing significance, there is still limited research specifically focused on the RO/RO shipping network and its impact on global trade. This paper studies the global RO/RO shipping network using AIS data on RO/RO vessels collected from 2020 to 2023. We construct a method based on the complex network theory and the graph feature extraction method to quantitatively assess the features of the RO/RO shipping network. This method assesses the complexity, sparsity, homogeneity, modularity, and hierarchy of the RO/RO shipping network across various ports and countries and employs the graph convolutional neural network (GCN) model to extract network features for community detection. This process enables the identification of port clusters that are frequently linked to RO/RO vessels, as well as regional transport modes. The paper’s findings support these conclusions: (1) From 2020 to 2023, the number of nodes in the RO/RO shipping network increased by 22%, primarily concentrated in African countries. The RO/RO shipping network underwent restructuring after the pandemic, with major complex network parameters showing an upward trend. (2) The RO/RO shipping network is complex, with a stable graph density of 0.106 from 2020 to 2023. The average degree increased by 7% to 4.224. Modularity decreased by 6.5% from 0.431 in 2022 to 0.403, while the hierarchy coefficient rose to 0.575, suggesting that post-pandemic, community routes have become more diverse, reflecting the reconstruction and maturation of the overall network. (3) The model yielded a silhouette coefficient of 0.548 and a Davies–Bouldin index of 0.559 using an improved automatic feature extraction method. In comparison between 2020 and 2023, the changes in the two indicators are small. This shows that GINs can effectively extract network features and give us results that we can understand for community detection. (4) In 2023, key communities divide the RO/RO shipping network, with one community handling 39% of global routes (primarily Europe–Asia), another community handling 23% (serving Asia–Pacific, Africa, and the Middle East), and a third community managing 38% (linking Asia, Europe, and South America).

The recent experience of lockdowns during COVID-19 highlights the prolonged impact a pandemic could have on ports and the shipping industry. This paper uses port call data derived from the Automatic Identification System (AIS) reports from the world’s 30 largest container ports to quantify both the immediate and longer-term impact of national COVID-19 lockdown policies on global shipping flows. The analysis uses the Difference-in-Difference (DID) and combined regression discontinuity design (RDD)-DID models to represent the effects of lockdown policies. The combination of RDD and DID models is particularly effective because it can mitigate time trends in the data, e.g., the Chinese New Year effect on Chinese ports. This study further examines the potential shock propagation effects, namely, how lockdown policy in one country (i.e., China) can affect the number of port calls in other countries. We categorize ports in other countries into a high-connectivity (with Chinese ports) group and a low-connectivity group, using a proposed connectivity index with China derived from individual vessel trajectories obtained from the AIS data. The results provide a clearly measurable picture of the kinds of trade shocks and consequent pattern changes in port calls over time caused by responses to lockdown policies of varying levels of stringency. We further document the existence of significant shock propagation effects. As the risk of pandemics rises in the twenty-first century, these results can be used by policy makers to assess the potential impact of different levels of lockdown policy on the maritime industry and trade flows more broadly. Maritime players can also use findings such as these to manage their capacity during lockdowns more effectively and to respond more flexibly to changing demand in seaborne transportation.

Maintaining the reliability of maritime transport is important as it plays an essential role in the world’s logistics. Shipping companies must also ensure the quality of liner service from the viewpoint of punctuality, as failure to take appropriate action against transport delays can lead to significant financial losses. This study quantitatively analyzes the behavior of containerships in response to global transport delays in 2021 caused by the COVID-19 pandemic using Automatic Identification System (AIS) data and port call history data. This study first comprehensively reveals the delay of containerships in 2021 by highlighting the increased variability in vessel arrival times, decreased handling capacities in specific ports, and increased navigation and anchorage times. Subsequently, this study clarifies how vessels recovered their schedule by increasing ship speeds or skipping some ports. We found vessel speeds susceptible depending on their delay in some specific voyages, such as long-distance cross-regional navigation.

Purpose. The purpose of the article is to analyze current trends in the development of container transportation in the world and in Ukraine, in particular in railway transport. Methodology. Analysis of official statistical data from domestic and international analytical institutions in order to build forecast models regarding the prospects for the development of container transportation volumes in the world and in Ukraine, as well as to identify potential areas for further research into relevant cargo flows. Results. Container transportation is a key logistical element of global foreign economic relations. World trade in containers has been showing steady growth since the mid-20th century. According to forecasts, by 2030, the volume of container operations in seaports will increase by almost 14% compared to 2019, reaching approximately USD 92 billion. Based on data for 2018-2024, a linear regression model was constructed, which indicates a stable positive dynamics of container transportation volumes by sea with an average annual growth of about 2 million TEU. Global container flows are distributed extremely unevenly: the main share falls on the East-West direction, that is, between the countries of Southeast Asia and the developed countries of the West. At the same time, there is a gradual increase in the volume of transportation in the South-South direction, between developing countries. In the global structure, the Ukrainian railway container transportation market occupies a small share, which is explained, in particular, by the consequences of the full-scale war in Ukraine. In 2023-2025, the vast majority of containers were transported by Ukrainian railways in domestic traffic. About 35% of containers are transported empty. The largest share of loaded containers is made up of grain, leguminous and oilseed crops, as well as ferrous metals and sunflower oil. The largest number of wagons with containers depart from port and border railway stations. In domestic traffic, the stations of Pryluky, Ternopil, Myrhorod, Zaporizhzhia-Live, as well as the railway hubs of Kyiv are most intensively operating. Among the main problems for a more intensive development of container railway transportation in Ukraine are the full-scale war, as well as the lack of developed terminal infrastructure, especially in the internal regions and on the western border. Originality. The article considers current trends and problems of the container transportation market in the world and in Ukraine, in particular during the period of full-scale Russian aggression. A predictive regression model for the volumes of the world container market is obtained. An analysis of the volumes of container transportation by Ukrainian railways by cargo nomenclature and departure stations is presented. Practical value. The results obtained allow us to proceed to the stage of modeling promising container flows and forming correspondences for research and evaluation of various technologies for organizing the transportation process.

One of the crucial challenges of maritime transport is digitalization through the implementation of proper IT solutions. Development and deployment of such solutions allows seaports to increase their overall efficiency, to lower costs and improve performance times of the intra-terminal operations, to improve information flow and decision-making and to decrease the environmental footprint by addressing the sustainability. Nevertheless, the digitalization levels are heterogeneous and depend on different factors, such as seaport size, cargo volumes and type, stakeholders’ needs, business models and investments. We propose a prototyping framework for agile smart service development relying on a set of standard and open-source technologies. The proposed framework is validated at The Port Network Authority of the Northern Tyrrhenian Sea (Port of Livorno) and the transferability of research results is assessed against the Port of Singapore, the most important hub of the Asian region.

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The maritime industry has been vital in facilitating and enabling economic development and global trade. Although the industry and its supply chain are not new concepts, they have gained significant attention from academic researchers over the past decade. As a result, numerous scholarly explorations and investigations have been published. This study aims to analyze publication trends, scientific impact, and existing themes and address gaps within maritime supply chain publications. a bibliometric method is applied to 382 articles extracted from two popular databases, Scopus and Web of Science. The study uncovered a growing focus on the maritime supply chain, with particular attention given to maritime logistics. The literature revealed several recurring themes: blockchain integration, supply chain risk management, and green logistics. However, there is still a need for more empirical investigation into sustainable performance, especially in areas like the green maritime supply chain. Future studies should expand on existing conceptual explorations and incorporate empirical investigations. The findings have two main benefits: they provide researchers with opportunities for further investigation and enable policymakers and port authorities to monitor global maritime supply chain trends and progress. By doing so, they can learn from others' initiatives and improve their current practices.

ABSTRACT This research aims to establish the link between blockchain technology adoption in the maritime and shipping industry and its impact on maritime supply chain integration via a systematic review of both the academic and practice literature. In total 148 articles were identified and analysed. Blockchain applications identified from the literature are categorized into three domains: document management, transaction management, and cargo/vessel/terminal operations. An analysis of the benefits and challenges that influence the deployment of blockchain technology for maritime supply chain integration leads to the development of an integrated and extended Technology, Organization, and Environment (TOE) framework. This study is among the first to examine the current state of blockchain diffusion within the maritime supply chain, making a significant contribution to the field. The extended TOE framework offers guidance for future research and understanding of the relationship between blockchain adoption and maritime supply chain integration. It can be used to assist organisations in successfully adopting blockchain technology in their supply chain operations.