Local AIS data analytics for efficient operation management in Vessel Traffic Service

Gangyan Xu, Fan Li, Chun Hsien Chen

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

1 Citation (Scopus)


Vessel Traffic Service (VTS) is vital for ensuring the safe and smooth maritime traffic in designated areas. With the rapid development of technologies and the extensive implementation of Automatic Identification System (AIS), a lot of data are available to support the decision-making processes in VTS. Although a lot of efforts have been made on exploring the data for various traffic management activities, few of them have been done using AIS data to facilitate the operation management in VTS. Focusing on this problem, this paper proposes an AIS data analytics framework to improve the efficiency of operation management in VTS. Through extracting the spatial-temporal data from discrete vessel kinematics status, the historical traffic analysis module is proposed, which could provide statistical information to support the planning issues in VTS. Besides, K-mean based property classification is introduced to transform the traffic data into easy-to-understand N-degree descriptions. Furthermore, to improve the ability of dealing with dynamics, a BPANN-based short-term traffic prediction module is also built. Finally, an experimental case study is given to verify the effectiveness of the proposed methods.

Original languageEnglish
Title of host publication2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509067800
Publication statusPublished - Aug 2017
Externally publishedYes
Event13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: 20 Aug 201723 Aug 2017

Publication series

NameIEEE International Conference on Automation Science and Engineering
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089


Conference13th IEEE Conference on Automation Science and Engineering, CASE 2017

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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