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In this paper, the authors perform a comprehensive literature review on the use of data obtained from the Automatic Identification System, with an emphasis on vessel route prediction and seaport operations. The usage of Automatic Identification System vessel’s position data in the vessel route prediction and seaport operations has been analyzed, to prove that Automatic Identification System data has a large potential to improve the efficiency of maritime transport. The authors concluded that proper vessel route prediction and route planning can improve voyage safety and reduce unnecessary costs. Furthermore, AIS can provide port authorities with early warnings, allowing them to take preemptive action to avoid possible congestions and unnecessary costs.

For developing national maritime traffic routes through the coastal waters of Korea, the customary maritime traffic flow must be accurately identified and quantitatively evaluated. In this study, the occupancy time of ships in cells was calculated through a density analysis based on automatic identification system data. The density map was statistically created by logarithmically transforming the density values and adopting standard deviation-based stretch visualization to increase the normality of the distribution. Many types of traffic routes such as open-sea, coastal, inland, and coastal access routes were successfully identified; moreover, the stretch color ramp ratio was reduced to identify routes having relatively high density. Adopting a single standard deviation and demonstrating the top 25% of color ramps, the analysis afforded the main routes through which customary traffic flows. This novel density analysis method and statistical visualization method is expected to be used for developing national maritime traffic routes and should ultimately contribute to maritime safety. Moreover, it provides a scientific means and simulator for determining the navigation area and analyzing conflicts with other activities in marine spatial planning.

In the extensive monitoring of maritime traffic, maritime management frequently encounters incomplete automatic identification system (AIS) data. This deficiency poses significant challenges to safety management, requiring effective methods to infer corresponding ship information. We tackle this issue using a classification approach. Due to the absence of a fixed road network at sea unlike on land, raw trajectories are difficult to convert and cannot be directly fed into neural networks. We devised a latitude–longitude gridding encoding strategy capable of transforming continuous latitude–longitude data into discrete grid points. Simultaneously, we employed a compression algorithm to further extract significant grid points, thereby shortening the encoding sequence. Utilizing natural language processing techniques, we integrate the Word2vec word embedding approach with our novel biLSTM self-attention chunk-max pooling net (biSAMNet) model, enhancing the classification of vessel trajectories. This method classifies targets into ship types and ship lengths within static information. Employing the Taiwan Strait as a case study and benchmarking against CNN, RNN, and methods based on the attention mechanism, our findings underscore our model’s superiority. The biSAMNet achieves an impressive trajectory classification F1 score of 0.94 in the ship category dataset using only five-dimensional word embeddings. Additionally, through ablation experiments, the effectiveness of the Word2vec pre-trained embedding layer is highlighted. This study introduces a novel method for handling ship trajectory data, addressing the challenge of obtaining ship static information when AIS data are unreliable.

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.

Recently, marine development plans such as offshore wind farms and marina port facilities have been established to use Korean coastal waters, and research on the development of operational ships such as autonomous ships and water-surface flying ships is being rapidly promoted. Since the marine traffic in Korean coastal waters is expected to increase, the government intends to construct a coastal maritime transportation network that connects Korean coastal waters to guarantee safe ship navigation. Therefore, this study used automatic-identification-system data analysis to obtain quantitative evaluation results on maritime traffic distribution characteristics and utilization levels for the entire Korean coastal waters in grid cell for greater consistency and compatibility. The characteristics of marine traffic distribution at a certain site in coastal Korean waters can be quantitatively examined using the findings of this study, and they may be used as grid-cell-type data-based information. Moreover, the vessel traffic index allows for extensive research while quickly understanding the present level of use of the passing ships by the sea area. In this regard, the findings of this study are expected to be useful for the future development of maritime transportation networks in Korean coastal waters.

With the development of bridge crossings over rivers, the accident of the vessel–bridge collision is increasing as well. It is important to assess probability of bridges colliding with passing ships. Firstly, the AIS (Automatic identify system) data was collected and decoded to obtain the dynamic information of the ships passing the bridge including the distributions of ships position, speed, and yaw angle, which are then compared with the value recommended by the AASHTO (American Association of State Highway and Transportation Officials) specification. The mainly influential parameters of ship–bridge collision obtained from AIS data are used to correct the variables in the risk assessment of AASHTO specification, which intends to improve the assessment accuracy by considering the actual information of passing vessels. The collision probability with and without considering the actual situations of passing ships are compared. It is found that the distribution and transit path of passing ships significantly influence the collision probability. To improve the risk assessment accuracy, it is suggested to use the actual distributions of passing ships from AIS data.

As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have been conducted relating to oceanic operations by ships, few have addressed maneuvering in ports. Therefore, in an effort to resolve this issue, this study explores ship trajectories derived from automatic identification systems’ data collected from ships arriving in and departing from the Busan New Port in South Korea. The collected data were analyzed by dividing them into port arrival and departure categories. To analyze ship trajectory patterns, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a machine learning clustering method, was employed. As a result, in the case of arrival, seven clusters, including the leg and turning section, were derived, and departure was classified into six clusters. The clusters were then divided into four phases and a pattern analysis was conducted for speed over ground, course over ground, and ship position. The results of this study could be used to develop new port maneuvering guidelines for ships and represent a significant contribution to the maneuvering practices of autonomous ships in port.

Monitoring maritime oil flow is important for the security and stability of energy transportation, especially since the “21st Century Maritime Silk Road” (MSR) concept was proposed. The U.S. Energy Information Administration (EIA) provides public annual oil flow data of maritime oil chokepoints, which do not reflect subtle changes. Therefore, we used the automatic identification system (AIS) data from 2014 to 2016 and applied the proposed technical framework to four chokepoints (the straits of Malacca, Hormuz, Bab el-Mandeb, and the Cape of Good Hope) within the MSR region. The deviations and the statistical values of the annual oil flow from the results estimated by the AIS data and the EIA data, as well as the general direction of the oil flow, demonstrate the reliability of the proposed framework. Further, the monthly and seasonal cycles of the oil flows through the four chokepoints differ significantly in terms of the value and trend but generally show an upward trend. Besides, the first trough of the oil flow through the straits of Hormuz and Malacca corresponds with the military activities of the U.S. in 2014, while the second is owing to the outbreak of the Middle East Respiratory Syndrome in 2015.

The promising outcomes of dynamical system identification techniques, such as SINDy (Brunton et al. in Proc Natl Acad Sci 113(15):3932–3937, 2016), highlight their advantages in providing qualitative interpretability and extrapolation compared to non-interpretable deep neural networks (Rudin in Nat Mach Intell 1(5):206–215, 2019). These techniques suffer from parameter updating in real-time use cases, especially when the system parameters are likely to change during or between processes. Recently, the OASIS (Bhadriraju et al. in AIChE J 66(11):16980, 2020) framework introduced a data-driven technique to address the limitations of real-time dynamical system parameters updating, yielding interesting results. Nevertheless, we show in this work that superior performance can be achieved using more advanced model architectures. We present an innovative encoding approach, based mainly on the use of Set Encoding methods of sequence data, which give accurate adaptive model identification for complex dynamic systems, with variable input time series length. Two Set Encoding methods are used: the first is Deep Set (Zaheer et al. in Adv Neural Inf Process Syst 30, 2017), and the second is Set Transformer (Lee et al. in: International conference on machine learning, PMLR, pp 3744–3753 2019). Comparing Set Transformer to OASIS framework on Lotka–Volterra for real-time local dynamical system identification and time series forecasting, we find that the Set Transformer architecture is well adapted to learning relationships within data sets. We then compare the two Set Encoding methods based on the Lorenz system for online global dynamical system identification. Finally, we trained a Deep Set model to perform identification and characterization of abnormalities for 1D heat-transfer problem.

This paper assesses the impact of a major disruptive event at the port of Gothenburg, Scandinavia’s largest container port. Automatic Identification System (AIS) data is analyzed, in combination with official port statistics on container handling in the four main container ports in Sweden, from 2014–2018. Particular attention is paid to the relationship between container volumes handled and calculated performance metrics at the specific times of the intense labour dispute at the port of Gothenburg during the periods Q2 (2016) and Q4 (2016)–Q2 (2017). The paper concludes that the decline in container volumes handled at Gothenburg over the period is specifically due to fewer ships calling at the port following each of the intense periods of the labour dispute. It is also concluded that the effect on competitor ports in the region were significant in terms of both increased volumes of gateway container traffic and the resulting short-term and medium term impacts on both port user profiles and port efficiency levels.