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Open AccessArticle

Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

by Miao Gao 1,2,3, Guoyou Shi 1,2,3,* and Shuang Li 1,3
1
Navigation College, Dalian Maritime University, Dalian 116026, China
2
Collaborative Innovation Research Institute of Autonomous Ship, Dalian Maritime University, Dalian 116026, China
3
Key Laboratory of Navigation Safety Guarantee of Liaoning Province, Dalian 116026, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4211; https://doi.org/10.3390/s18124211
Received: 10 October 2018 / Revised: 12 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
(This article belongs to the Special Issue Deep Learning Remote Sensing Data)
The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy. Through the “forget gate” of the long short-term memory (LSTM) unit, the common behavioral patterns were remembered and unique behaviors were forgotten, improving the universality of the model. The BI-LSTM-RNN was trained using 2015 AIS data from Tianjin Port waters. The results indicate that the BI-LSTM-RNN effectively predicted the navigational behaviors of ships. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could potentially be applied as the predictive foundation for various intelligent systems, including intelligent collision avoidance, vessel route planning, operational efficiency estimation, and anomaly detection systems. View Full-Text
Keywords: ship behavior; machine learning; online prediction; AIS sensor data; big data ship behavior; machine learning; online prediction; AIS sensor data; big data
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MDPI and ACS Style

Gao, M.; Shi, G.; Li, S. Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network. Sensors 2018, 18, 4211.

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