Pubblicazione scientifica

Vessel track information mining using AIS data

FILONE TECNOLOGICO	TEMA
produzione e cattura

RIASSUNTO

In recent years, vessel traffic and maritime situation awareness become more and more important for countries across the world. AIS data contains much information about vessel motion and reflects traffic characteristics. In this paper, data mining is introduced to discover motion patterns of vessel movements. Firstly, we do statistical analysis for large scale of AIS data. Secondly, we use association rules to analyze the frequent moving status of vessels. We extend the dimensions of data features, improve the algorithm in efficiency and import the concept of time scale in the algorithm based on the previous relative work. Thirdly, we introduce Markov model to make supplement for the association rules. The prediction results in the Markov process are further used to do the anomaly detection. The method in this paper provides novel idea for the research in AIS data and the management of maritime traffic.

DATA

Data di pubblicazione: 01/09/2014

AUTORI

FENG DENG
SITONG GUO
YONG DENG
HANYUE CHU
QINGMENG ZHU
FUCHUN SUN

ENTE DI AFFERENZA

SCI & TECH ON INTEGRATED INF SYS LAB INST SOFTWARE BEIJING CHINA
UNIV CHINESE ACAD SCI BEIJING CHINA

RIVISTA

2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI) (Page(s): 1-6)

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