Vessel route anomaly detection with Hadoop MapReduce

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

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.

<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 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.

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

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

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%.

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

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.

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.

In order to better mine information from the data of automatic identification system (AIS) and scientifically perceive the water traffic situation, several common clustering algorithms, such as K-means algorithm and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, are studied and analyzed, and the advantages and disadvantages of each algorithm and applicable scenarios are compared. Taking the actual ship's AIS trajectory data as an example, each algorithm is used to verify its characteristics, which provides convenience for further information mining of trajectory data.

In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.

Image segmentation is the process to divide a digital image into a number of regions for further usage. Color images can be segmented by applying density-based clustering methods, e.g. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which is used to identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the size of image input is normally very large. Self-Organizing Map (SOM) is a dimensionality reduction method which can be applied to reduce the dimensions of image processing tasks. This paper proposes a hybrid method of SOM and DBSCAN (SOM-DBSCAN) for image segmentation. To evaluating the usability of the proposed SOM-DBSCAN method, four images are used to benchmark image segmentation.

The size and complexity of today's mobile networks is continuously growing, and with it the cost of planning and operating these networks. Network planning and engineering have to reach a delicate balance between cost, network performance, and up-time of business critical services. With this rapid development in mobile network we need effective network planning tool to satisfy the need of customers. However, deciding upon the optimum placement for the base stations to achieve best services while reducing the cost is a complex task requiring vast computational resource. This paper addresses antenna placement problem or the cell planning problem, involves locating and configuring infrastructure for mobile networks by modified the original density-based Spatial Clustering of Applications with Noise algorithm. The density-based Spatial Clustering of Applications with Noise original algorithm has been modified and a new algorithm Clustering Density Base in Presence of Obstacles has been proposed by the authors to solve the problem of obstructs in Mobile Network Planning. Implementation of this algorithm to a real case study is presented. Results demonstrate the effectiveness and flexibility of the modifying algorithm in tackling the important problems of mobile network planning.

Design of computationally efficient density-based clustering algorithms