Big Data Framework for Abnormal Vessel Trajectories Detection using Adaptive Kernel Density Estimation

Sidibé, Abdoulaye; Gao, Shu; Ma, Yunzhao; Wanqi, Wei

doi:10.1145/3291801.3291816

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…The ability to estimate a probability distribution from a single data sample is critical across diverse fields of science and finance (Munkhammar et al, 2017;Sidibé et al, 2018;Tang et al, 2016;Cavuoti et al, 2017). Estimating the parameters of the underlying density function becomes increasingly difficult when there is no prior information about the number of parameters, as the shape and complexity must also be inferred from that data.…”

Section: Introductionmentioning

confidence: 99%

PDFEstimator: An R Package for Density Estimation and Analysis

Farmer¹,

Jacobs²

2022

The R Journal

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This article presents PDFEstimator, an R package for nonparametric probability density estimation and analysis, as both a practical enhancement and alternative to kernel-based estimators. PDFEstimator creates fast, highly accurate, data-driven probability density estimates for continuous random data through an intuitive interface. Excellent results are obtained for a diverse set of data distributions ranging from 10 to 10 6 samples when invoked with default parameter definitions in the absence of user directives. Additionally, the package contains methods for assessing the quality of any estimate, including robust plotting functions for detailed visualization and trouble-shooting. Usage of PDFEstimator is illustrated through a variety of examples, including comparisons to several kernel density methods.

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Section: Introductionmentioning

confidence: 99%

PDFEstimator: An R Package for Density Estimation and Analysis

Farmer¹,

Jacobs²

2022

The R Journal

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“…An unsupervised model called Traffic Route Extraction and Anomaly Detection (TREAD) is establish in [19] to automatically learn maritime traffic patterns. To reduce the training time, the water area is partitioned in [20] to establish a training framework based on Adaptive Kernel Density Estimation (AKDE). The combination of Supervised-Learning-Based and Unsupervised-Learning-Based methods results in a hybrid approach.…”

Section: Introductionmentioning

confidence: 99%

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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