Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data

Ran Yan, Shuaian Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Maritime safety and security are gaining increasing concern in recent years. There are a growing number of studies aiming at improving situational awareness in the maritime domain by identifying vessel anomaly behaviors based on the data provided by the Automatic Identification System (AIS). Two types of data-driven methods are most popular in vessel anomaly detection based on AIS data: the statistical methods and machine learning methods. To improve the detection model efficiency and accuracy, hybrid models are formed by combining different types of methods. In order to incorporate expert knowledge, interactive systems are also designed and realized. In this paper, we provide a review of the popular statistical and machine learning models, as well as the hybrid models and interactive systems based on the data-driven methods used for anomaly detection based on AIS data.

Original languageEnglish
Title of host publicationSmart Transportation Systems 2019
EditorsLakhmi C. Jain, Xiaobo Qu, Robert J. Howlett, Lu Zhen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9789811386824
Publication statusPublished - Jun 2019
Event2nd KES International Symposium on Smart Transportation Systems, KES-STS 2019 - St. Julians, Malta
Duration: 17 Jun 201919 Jun 2019

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


Conference2nd KES International Symposium on Smart Transportation Systems, KES-STS 2019
CitySt. Julians

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

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