Data mining approach to shipping route characterization and anomaly detection based on AIS data

A maritime traffic route extraction method based on density-based spatial clustering of applications with noise for multi-dimensional data

<span lang="EN-US">Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.</span>

The increasing maritime traffic density in the Makassar Strait, particularly due to the development of the Hub Port at Indonesia’s new capital (IKN), necessitates a structured Traffic Separation Scheme (TSS) to ensure navigational safety. This study aims to design a preliminary TSS by integrating hydro-oceanographic data, vessel traffic analysis, and maritime risk assessment. A qualitative research methodology employing case studies and literature reviews was applied. Vessel movement patterns were analyzed using data from the Indonesian Maritime Security Agency (BAKAMLA RI) and vessel tracking applications. Safety assessments were conducted based on historical collision records and probabilistic modeling of grounding, stranding, and vessel collisions. Hydro-oceanographic data, including wind speed, wave height, and tidal currents, were sourced from secondary data and validated using numerical modeling. The findings suggest that the Makassar Strait TSS should consist of two separate lanes, each 3 km wide, with dedicated northbound and southbound routes. The design considers navigational obstacles such as subsea pipelines, offshore platforms, and coral reefs, ensuring safe passage. Hydro-oceanographic analysis confirms that prevailing conditions, including wind speeds below 15 knots and maximum wave heights of 1.25 meters, comply with recommended maritime safety standards. Additionally, statistical evaluations of vessel movement and collision probability indicate that a 3 km-wide channel effectively mitigates accident risks. This study provides an initial framework for optimizing TSS implementation in one of Indonesia’s most strategic waterways. The results highlight the importance of structured traffic management in reducing navigational hazards and improving maritime efficiency. The proposed TSS design aligns with international safety regulations and aims to support the long-term development of the IKN Hub Port as a critical maritime node. However, this study relies on secondary data and does not incorporate real-time traffic monitoring or high-resolution hydrographic surveys. Future research should integrate real-time Automatic Identification System (AIS) data and in-situ hydrographic measurements to refine the TSS structure. Additionally, further hydrodynamic simulations should be conducted to enhance route optimization under extreme weather conditions.

The trajectory data of vessel AIS (automatic identification system) has important theoretical and application value for information supporting decisions. However, large sizes lead to difficulties in storing, querying, and processing. To solve the problems of high compression ratio and longtime consumption of the existing online trajectory compression algorithm, an SPM (scan-pick-move) trajectory data compression algorithm added sliding window is proposed. In order to better compress vessel trajectory data regarding compression efficiency, the sliding window is added to the classical SPM algorithm. In order to reduce trajectory data storage space, the maximum offset distance reference trajectory point is used as the criterion of whether the current trajectory point can be compressed. In this paper, the multi-dimensional space-time characteristics of trajectory data, such as distance error, compression ratio and compression time, are selected to evaluate the trajectory compression method from three levels: geometric characteristics, motion characteristics and compression efficiency. Compared with the existing SPM trajectory data compression algorithm, parallel experiments are conducted based on AIS data gathered over the duration of a month in the Japan Osaka Bay. The SPM trajectory compression algorithm added sliding window can significantly reduce the compression time and outperforms other existing trajectory compression algorithms in term of average compression error at high compression strengths. Also, the proposed method has high compression efficiency in the range of commonly used compression thresholds.

Automatic identification system (AIS) data record a ship’s position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship’s voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters.

Sunda Strait is a busy channel where cargo vessels could probably have a crossing situation with roro ferries. Due to a very limited record of the actual crossing collisions in Sunda Strait, this study performs a Probabilistic Risk Assessment (PRA) of near miss crossing situations in Sunda Strait due to the Traffic Separation Scheme (TSS) that has been set since July 1st, 2020. The analysis is based on the Automatic Identification System (AIS) data during three time-intervals (TI), the first two TIs represented the condition before the TSS came into force, while the last TI was taken after the TSS has been set. The traffic in Sunda Strait was categorized to eight vessel courses, two conditions and seven crossing zones. We proposed a new perspective for the evaluating the TSS by looking at the crossing situation with three different bases, namely crossing zone basis, course basis, and vessel type basis. The probability of a crossing situation was calculated based on the hour basis for each time interval. The UK HSE standard for individual risk is utilized and it is found that the TSS effectively reduced the frequency level of crossing situation from unacceptable to ALARP in CZ 1, 2, and 4. While in CZ 3, the frequency is decreased dramatically from unacceptable to acceptable level.

Data mining approach for automatic ship-route design for coastal seas using AIS trajectory clustering analysis

The dynamic uncertainty caused by tidal current in river estuary is affected, A method for dynamic identification of main route of river estuary segment based on AIS (Automatic Identification System) is proposed by using the AIS navigation trajectory of the most recent typical ship and the time point of large Ocean currents. Firstly, clustering algorithm is used to analyze the navigation trajectory of typical ships in the estuary recently, and the track of the estuary channel is identified, then, cubic spline interpolation algorithm is used to fit the center trajectory points obtained by DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and get the main route, combined with the current characteristics of large ocean current, the main route is modified appropriately, therefore, the effect of frequent changes on the channel of river estuary is solved, and the validity of this method is verified through the analysis of the measured data.

The new routeing measures in Sunda Strait, that consists of traffic separation scheme (TSS), precautionary area (PA), and inshore traffic zones (ITZ), have been implemented since July 2020. The Automatic Identification System (AIS) data is utilized to understand the traffic pattern in this area. One dataset before and one dataset after the implementation of TSS is then converted into a vessel trip database. Eight vessel courses, two positions, and seven crossing zones are determined from the density plot to categorise the vessel trips. This study introduced a different perspective to assess the crossing situation by using three point-of-views to calculate the probability, namely crossing zone basis, course basis, and vessel type basis. The analysis result shows that the crossing situation is mostly occurred in the precautionary area of the new routeing measure.

Path planning for large ships in inland waterways considering risk assessment of AIS data

In recent years, the prediction of ship trajectory based on automatic identification system (AIS) data has become an important area of research. Among the existing studies, most focus on a single ship to extract features and train models for trajectory prediction. However, in a real situation, AIS contains a variety of ships and trajectories that need a general model to serve various cases. Therefore, in this paper, we include an attentional mechanism to train a multi-trajectory prediction model. There are three major processes in our model. Firstly, we improve the traditional density-based spatial clustering of applications with noise (DBSCAN) algorithm and apply it to trajectory clustering. According to the clustering process, ship trajectories can be automatically separated by groups. Secondly, we propose a feature extraction method based on a hierarchical clustering method for a trajectory group. According to the extraction process, typical trajectories can be obtained for individual groups. Thirdly, we propose a multi-trajectory prediction model based on an attentional mechanism. The proposed model was trained using typical trajectories and tested using original trajectories. In the experiments, we chose nearby port waters as the target, which contain various ships and trajectories, to validate our model. The experimental results show that the mean absolute errors (MAEs) of the model in longitude (°) and latitude (°) compared with the baseline methods were reduced by 8.69% and 6.12%.

Automatic Identification System (AIS) is a telecommunication system created to allow ships to communicate with each other by exchanging messages containing information such as vessel’s ID, position, speed, heading, etc. AIS is useful in many cases, such as early ship collision detection. However, its terrestrial segment’s drawback is a relatively low range (about 74 km, roughly 40 nautical miles). Satellite Automatic Identification System (SAT-AIS) was introduced to overcome this limitation, but it suffers from its own problem known as packet collision. The satellite receives messages from multiple terrestrial cells and communication is synchronized within such cells, but not between them, thus messages got lost or damaged when they appear at the satellite at the same time. In this paper, we present a machine learning-based approach to reconstruct those missing messages and deeply investigate whether or not a density-based spatial clustering of applications with noise (DBSCAN) can be considered in the first stage of the reconstruction. The experiment focuses on findig the optimal parameters for the clustering, running it both on original and damaged data to finally ascertain that DSBCAN can distinguish individual trajectories in a dataset that can be further reconstructed.

A method for simplifying ship trajectory based on improved Douglas–Peucker algorithm

This study investigates the application of maritime trajectory clustering in the Straits of Malacca and Singapore (SOMAS), aiming to identify and categorize key movement patterns in this densely trafficked and complex region. Using Automatic Identification System (AIS) data, the research employs Dynamic Time Warping (DTW) to measure trajectory similarity and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for clustering. Due to the wide variation in trajectory lengths across the region, a statistical grouping technique based on the interquartile range (IQR) is applied to improve clustering performance and manage trajectory diversity. The results demonstrate the successful identification of major traffic routes within the study area, providing valuable insights into dominant vessel movement behaviours and navigation patterns. These findings are essential for supporting maritime traffic analysis, thereby enhancing safe, efficient, and sustainable traffic management in high-density international waterways.

We present a two-level approach to detect abnormal activities for vessels' routes. The data is obtained from the Automatic Identification System (AIS) which is required to be installed on vessels over specific gross tonnage. In the first level, we develope a Clustering algorithm: Density-based Spatial Clustering of Applications with Noise considering Speed and Direction (DBSCAN_SD). This algorithm is applied to pre-cluster the data points. Using domain knowledge in maritime, experts adjust the results produced by DBSCAN_SD with extra features. In this way, we get the optimal labeling result about whether a data point is normal or abnormal. In the second level, we use the labeled data generated in the first level to train the Parallel Meta-Learning (PML) algorithm on Hadoop. The results show that both accuracy and time complexity results are improved when we increase the number of nodes in a cluster.