[1]REN Chengjie,CHEN Huaixin,XIE Wei.Ship route extraction based on GRU auto-encoder[J].CAAI Transactions on Intelligent Systems,2022,17(6):1201-1208.[doi:10.11992/tis.202107006]
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Ship route extraction based on GRU auto-encoder

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 6 Page number: 1201-1208 Column: 学术论文—智能系统 Public date: 2022-11-05
Title:
Ship route extraction based on GRU auto-encoder
Author(s):
REN Chengjie1;  CHEN Huaixin1;  XIE Wei2
1. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China;
2. The 10th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China
Keywords:
route extraction; automatic identification system; data mining; gate recurrent unit auto-encoder; deep feature; decoding inversion; DBSCAN algorithm; trajectory clustering
CLC:
TP18
DOI:
10.11992/tis.202107006
Abstract:
The automatic identification system (AIS) data consists of a large amount of information associated with shipping behavior. A wide range of applications in maritime supervision and ship surveys can be made use of by extracting useful ship routes from AIS data. The gate recurrent unit auto-encoder (GRU-AE) is the foundation of a method for ship route extraction that is suggested. First, the GRU encoder is employed to encode the original trajectory data into deep feature information in a unified form; then, deep feature information is clustered using the DBSCAN algorithm; and finally, the deep feature cluster center is inverted through the decoder to produce the corresponding ship route, to achieve the mining of the ship trajectory pattern in the massive AIS data. Taking the Port of Boston as an example, more than 100,000 AIS ship navigation data in one year are examined. Experiments reveal that this technique can cluster and extract route data of various lengths and can support the research of ship trajectory abnormality identification, path planning, position prediction, etc., revealing good application adaptability.
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