Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Chen, Xinqiang; Ling, Jun; Yang, Yongsheng; Zheng, Hailin; Xiong, Pengwen; Postolache, Octavian

doi:10.1155/2020/7191296

Cited by 27 publications

(20 citation statements)

References 32 publications

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“…To further investigate the performance of the proposed method compared with the conventional anomaly detection approach that is frequently utilized in the pre-processing of AIS data, in this research, we conducted a comparison between the kinematic-based method proposed in our research and a typical rule-based anomaly detection algorithm such as the one utilized in [8]. Three of the same case trajectories are utilized in this section to provide clear results for the comparison of their performance in terms of the detection capability for location and velocity anomalies, respectively.…”

Section: Comparison With the Rule-based Detection Methodsmentioning

confidence: 99%

“…In general, those methods that correspond to the knowledge-driven approaches can be regarded as rule-based methods. The simplest way to conduct anomaly detection for AIS data is to use a predefined data range to determine and exclude the outliers [8], which is efficient but has relatively poor performance in terms of its accuracy and reliability. In [9], the authors considered the geometric shape of a ship trajectory and proposed a vector-based method to detect anomalies.…”

Section: Introductionmentioning

confidence: 99%

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An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

Guo

Mou

Chen

et al. 2021

JMSE

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With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS trajectory is proposed, making comprehensive use of the kinematic information of the ship in the AIS data. The method employs three steps to obtain non-error AIS trajectories: (1) data preprocessing, (2) kinematic estimation, and (3) error clustering. It should be noted that steps (2) and (3) are involved in an iterative process to determine all of the abnormal data. A case study is then conducted to test the proposed method on real-world AIS data, followed by a comparison between the proposed method and the rule-based anomaly detection method. As the processed trajectories show fewer abnormal features, the results indicate that the method improves performance and can accurately detect as much abnormal data as possible.

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Section: Comparison With the Rule-based Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

Guo

Mou

Chen

et al. 2021

JMSE

View full text Add to dashboard Cite

show abstract

“…[53]. Anomaly detection in preprocessing refers to using methods to detect and clean anomalies in trajectory data [54,55]. To correct the data outliers, the paper applies the moving average method [55] to the raw AIS data.…”

Section: B Data Sources and Preprocessesmentioning

confidence: 99%

“…Anomaly detection in preprocessing refers to using methods to detect and clean anomalies in trajectory data [54,55]. To correct the data outliers, the paper applies the moving average method [55] to the raw AIS data. Also, the wrong AIS messages, in which the length of their MMSI number is not nine-digits, are eliminated according to the length of the MMSI number.…”

Section: B Data Sources and Preprocessesmentioning

confidence: 99%

A Contextual Hybrid Model for Vessel Movement Prediction

2021

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Predicting the movement of the vessels can significantly improve the management of safety. While the movement can be a function of geographic contexts, the current systems and methods rarely incorporate contextual information into the analysis. This paper initially proposes a novel context-aware trajectories' simplification method to embed the effects of geographic context which guarantees the logical consistency of the compressed trajectories, and further suggests a hybrid method that is built upon a curvilinear model and deep neural networks. The proposed method employs contextual information to check the logical consistency of the curvilinear method and then, constructs a Context-aware Long Short-Term Memory (CLSTM) network that can take into account contextual variables, such as the vessel types. The proposed method can enhance the prediction accuracy while maintaining the logical consistency, through a recursive feedback loop. The implementations of the proposed approach on the Automatic Identification System (AIS) dataset, from the eastern coast of the United States of America which was collected, from November to December 2017, demonstrates the effectiveness and better compression, i.e. 80% compression ratio while maintaining the logical consistency. The estimated compressed trajectories are 23% more similar to their original trajectories compared to currently used simplification methods. Furthermore, the overall accuracy of the implemented hybrid method is 15.68% higher than the ordinary Long Short-Term Memory (LSTM) network which is currently used by various maritime systems and applications, including collision avoidance, vessel route planning, and anomaly detection systems.

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“…Simultaneously, it is increasingly becoming a challenging issue to efficiently utilize AIS data and provide vessel operators with more effective and convenient navigation services. Especially, the vessel trajectory information-based AIS data play a crucial role in the field of transportation studies for significant guidance of navigation behavior analyses [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data

Zuo

2022

Journal of Advanced Transportation

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With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.

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Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Cited by 27 publications

References 32 publications

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

A Contextual Hybrid Model for Vessel Movement Prediction

Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data

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