Ship Route Planning Using Historical Trajectories Derived from AIS Data

He, Yan Kang; Zhang, Di; Zhang, Jinfen; Zhang, M.Y.; Li, T.W.

doi:10.12716/1001.13.01.06

Cited by 16 publications

(3 citation statements)

References 12 publications

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“…For ship trajectory prediction research [6], [7], [8], Volkova et al [9] input the ship positioning information from the AIS data into a neural network for ship trajectory prediction to solve the weak positioning problem of the satellite signals obscured by obstacles in inland waters. For route planning [10], [11], [12], Zhang et al [13] proposed a new automatic maritime route generation algorithm. In this algorithm, given a set of ship track AIS data, the data are first compressed and clustered, and then an ant colony algorithm is used to perform a route search on the clustered clusters and recommend a better route.…”

Section: Introductionmentioning

confidence: 99%

Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm

et al. 2023

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The automatic identification system (AIS) provides a massive database for ocean science. The original AIS data are redundant. Direct use will cause a waste of data storage space and computation costs; hence, data compression must be performed. The Douglas-Peucker algorithm (DP) is an effective trajectory compression algorithm that can well preserve the spatial characteristics of a trajectory but has the following shortcomings: first, it has poor track recovery when compressing multi-turn routes; second, it does not consider the ship speed and heading; and third, it may have the wrong result of the compressed trajectory crossing the obstacle. To address these situations, this study proposes a multi-objective peak DP algorithm (MPDP) that adopts a peak sampling strategy, considers three optimization objectives (spatial characteristics, heading and speed) of trajectory and adds an obstacle detection mechanism to realize a compression algorithm more suitable for curved trajectories. The classical DP algorithm is compared with the MPDP algorithm by simulating trajectory and real trajectory experiments. The results show that the MPDP algorithm optimizes the length loss rate, simultaneous Euclidean distance, and average deviations of the speed and the heading while maintaining a high compression rate similar to that of the DP algorithm. Moreover, it can also successfully avoid obstacles. The optimization effect is most obvious for the multi-turn or hovering trajectory. The optimization rate of length loss, synchronous Euclidean distance, and average deviation of the heading can reach 40%.INDEX TERMS AIS data, Douglas-Peucker algorithm (DP), multi-objective peak Douglas-Peucker algorithm (MPDP), trajectory compression.

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Section: Introductionmentioning

confidence: 99%

Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm

et al. 2023

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“…In terms of ship trajectory prediction [11][12][13], Suo et al [14] extracted a series of trajectories from AIS ship data (i.e., latitude, longitude, speed, and heading), derived the main trajectories by applying the DBSCAN algorithm and finally introduced a deep learning framework and Gate Recirculation Unit model for predicting ship trajectories. In terms of ship route planning [15][16][17], Zhang et al [18] proposed a shortest-path planning method based on AIS data, establishing a low-precision environment model, and determining the grid area of the shortest path through an ant colony algorithm to reduce the amount of computation and ultimately determine the optimal path under a finer environment model through the A* algorithm. Regarding traffic management [19], Xu et al [20] proposed a framework based on AIS data analysis, including a historical traffic analysis module, K-means based attribute classification, and short-term traffic prediction module based on Back-Propagation Artificial Neural Network, to improve the efficiency of operations management in the Vessel Traffic Service.…”

Section: Introductionmentioning

confidence: 99%

Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data

Zhang,

Yuan,

et al. 2023

IEEE Access

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Automatic Identification System (AIS) can obtain a large amount of data on ship trajectories and movement characteristics, which provides data support for ship route planning, navigation safety, and maritime traffic control. Therefore, it is of great importance to analyze and cluster the AIS data to obtain an understanding of the movement behavior of ships. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is capable of discovering arbitrarily shaped clusters and is suitable for clustering ship trajectories. However, the traditional DBSCAN algorithm suffers from the following shortcomings. First, the DBSCAN algorithm requires the input of a similarity matrix between the trajectories, and the similarity matrix obtained from the traditional Hausdorff distances does not identify the trajectory directions. Second, the DBSCAN algorithm is sensitive to the input parameters, and the clustering results obtained from different datasets with the same parameters vary widely. Finally, the DBSCAN algorithm is poor at clustering multidensity distribution datasets. To address these shortcomings, this study proposes a multi-density adaptive trajectory clustering (MDA-Traclus) algorithm, which considers the multi-density distribution trajectory dataset, adaptively determines the input parameters, and adds a trajectory direction identification mechanism to realize the clustering of trajectory clusters with different directions and different densities. Here, the classical DBSCAN algorithm, KANN-DBSCAN algorithm, and MDA-Traclus algorithm are compared and analyzed using four manually labeled trajectory datasets and a real trajectory dataset. The results show that the MDA-Traclus algorithm and KANN-DBSCAN algorithm are able to automatically determine the input parameters when clustering trajectory datasets with uniformly distributed densities and achieve the same clustering effect as the DBSCAN algorithm. When the trajectory density distribution is not uniform and the directions of the trajectory clusters are not consistent, the MDA-Traclus algorithm can effectively identify the low-density regions, differentiate the different directions of trajectories, and improve the clustering of the trajectories. INDEX TERMSAIS data, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, multi-density adaptive trajectory clustering (MDA-Traclus) algorithm, trajectory clustering.

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“…Another example of analysis of both real-time and historical data is an identification of abnormal vessels' activity that may lead to the detection of an act of piracy (Lane et al 2010). On the other hand, AIS data might also be useful in a research of industrial usage in the form of maritime traffic analysis -prediction of the load in seaports and its optimization (Millefiori et al 2016) or route planning (He et al 2019).…”

Section: Introductionmentioning

confidence: 99%

K-means clustering for SAT-AIS data analysis

Mieczyńska

Czarnowski

2021

WMU J Marit Affairs

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The paper deals with a problem of automatic identification system (AIS) data analysis, especially eliminating the impact of AIS packet collision and detecting existing outliers in AIS data. To solve this problem, a clustering-based approach is proposed. AIS is a system that supports the exchange of information between vessels about their trajectories, e.g. position, speed or course. However, SAT-AIS, which enables the system to work on a global scale, struggles against packet collisions due to the fact that the satellite, which receives AIS data from ships, has a field of view that covers multiple areas that are not synchronized among themselves. As a result, the received data is difficult to process by AIS receivers, because most of the messages have a character of noise. In this paper, results of a computational experiment using k-means algorithm for packet recovery and for dealing with noise have been presented. The outcome proves that a clustering-based approach could be used as an initial step in AIS packet reconstruction, when the original data is incorrect.

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Ship Route Planning Using Historical Trajectories Derived from AIS Data

Cited by 16 publications

References 12 publications

Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm

Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm

Multi-Density Adaptive Trajectory Clustering Algorithm for Ships Based on AIS Data

K-means clustering for SAT-AIS data analysis

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