Semantic representation of ship behavior based on context autoencoder

MA Jie; HE Mu-rong; JIA Cheng-feng; LI Wen-kai; ZHANG Yu

doi:10.19818/j.cnki.1671-1637.2022.04.026

Volume 22 Issue 4

Aug. 2022

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Article Navigation > Journal of Traffic and Transportation Engineering > 2022 > 22(4): 334-347

MA Jie, HE Mu-rong, JIA Cheng-feng, LI Wen-kai, ZHANG Yu. Semantic representation of ship behavior based on context autoencoder[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 334-347. doi: 10.19818/j.cnki.1671-1637.2022.04.026

Citation:

MA Jie, HE Mu-rong, JIA Cheng-feng, LI Wen-kai, ZHANG Yu. Semantic representation of ship behavior based on context autoencoder[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 334-347. doi: 10.19818/j.cnki.1671-1637.2022.04.026

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Semantic representation of ship behavior based on context autoencoder

doi: 10.19818/j.cnki.1671-1637.2022.04.026

MA Jie^{1, 2, 3
,},
HE Mu-rong¹,
JIA Cheng-feng¹,
LI Wen-kai¹,
ZHANG Yu^{2, 3, 4}

1.
School of Navigation, Wuhan University of Technology, Wuhan 430063, Hubei, China
2.
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, Hubei, China
3.
National Engineering Research Center for Water Transportation Safety, Wuhan University of Technology, Wuhan 430063, Hubei, China
4.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, Hubei, China

Funds:

National Key Research and Development Program of China 2021YFB3901504

National Natural Science Foundation of China 52271366

National Natural Science Foundation of China 51679182

More Information

Author Bio:
MA Jie(1978-), male, professor, PhD, majie@whut.edu.cn
Received Date: 2022-02-09
Available Online: 2022-10-08
Publish Date: 2022-08-25

Abstract

Abstract

Considering the temporal correlation of ship behavior, a semantic representation model based on the context autoencoder (SRCAE) was proposed for ship behavior. The behavioral feature parameters, such as the longitude, latitude, speed, as well as the course, were extracted to establish the behavioral feature sequence. The behavioral feature sequence was divided into the center ship behavior and context ship behavior via the continuous bag-of-words (CBOW) model. The deep autoencoder (AE) networks were utilized to construct the semantic representation model of context ship behavior, and the encoded center ship behavior obtained from the model was output as the representation vector. The clustering algorithm was employed to establish the ship behavior dictionary. The South Passage Intersection Water of the Yangtze Estuary was selected as the research object, and the data from the automatic identification system (AIS) for ships were employed for verification of the proposed model and method. Analysis results show that the context relationships between ship behaviors can be effectively represented by the proposed SRCAE model, and the representation error of the SRCAE model is lower than that of the traditional AE model and long short-term memory autoencoder (LSTMAE) model. Three clustering algorithms, namely, k-means, Gaussian mixture model (GMM), and kernel k-means, were used to extract the ship behavior dictionary. Compared with the original data, the representation vectors generated by the SRCAE model are easier to distinguish different ship behavior patterns, among which the effect of k-means is the best, and the Silhouette coefficient (SC), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI) of k-means reach 0.384, 18.308, and 0.531, respectively. A total of 30 types of composite behaviors are generated, such as steering acceleration, steering deceleration, straight-ahead acceleration, straight-ahead deceleration, and so on and the combination relationships of ship behavior words under different behavior patterns are effectively extracted.
- ship automatic identification system,
- ship behavior,
- autoencoder,
- continuous bag-of-words model,
- semantic representation

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References(31)

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Semantic representation of ship behavior based on context autoencoder

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