International Journal of Transportation Engineering and Technology
Volume 6, Issue 1, March 2020, Pages: 16-21
Received: Feb. 6, 2020;
Accepted: Feb. 21, 2020;
Published: Feb. 28, 2020
Views 259 Downloads 189
Sun Yang, Merchant Marine College, Shanghai Maritime University, Shanghai, China
Peng Xinya, Merchant Marine College, Shanghai Maritime University, Shanghai, China
Ding Zexuan, Merchant Marine College, Shanghai Maritime University, Shanghai, China
Zhao Jiansen, Merchant Marine College, Shanghai Maritime University, Shanghai, China
AIS (Automatic Identification System) is a mandatory navigational equipment on board according to SOLAS (Safety of Life at Sea) convention. It is an automatic tracking system that uses VHF (Very High Frequency) transponders on ships and is used by VTS (Vessel Traffic Services) for monitor vessel movements. Existing AIS data has some principle defects due to radio propagation. This paper provides an approach to predict ship behavior with AIS data. In order to solve the problem that traditional ship behavior prediction needs to establish complex ship motion model, a new ship behavior prediction method based on LSTM (Long Short-Term Memory, LSTM) neural network model of machine learning is proposed. LSTM is the optimization model of RNN (Recurrent Neural Networks). Unlike standard feedback neural networks, LSTM has feedback connections. It can not only process single data points, but also entire sequences of data. These prominent features just match the characteristics of AIS data. The LSTM neural network prediction model is established and the shore is used. Based on the real data of AIS (Automatic Identification System) which ships engaged in the waters of South China Sea, the time series of ship behavior characteristics are extracted to train the model and validate the data. The training data is grouped by MMSI (Maritime Mobile Service Identity) and ensure the equal interval requirements of the ship's navigation behavior sequence data. This paper presents 4 figures with the parameter course, speed, position and the loss curve of LSTM training and testing. The results show that the model has a high accuracy and avoids the complicated process of ship motion modeling. The predicted results can improve the supervision of VTS (Vessel Traffic Services) and play a high practical application value in early warning of ship collision, SAR (Search and Rescue) operation and safety-related issues.
An Approach to Ship Behavior Prediction Based on AIS and RNN Optimization Model, International Journal of Transportation Engineering and Technology.
Vol. 6, No. 1,
2020, pp. 16-21.
Xu T, Cai F, Hu Q, et al. Research on estimation of AIS vessel trajectory data based on Kalman filter algorithm [J]. Modern Electronics Technique, 2014 (5): 97-100.
Xu T, Liu X, Yang X, et al. BP neural network-based ship track real-time prediction [J]. Journal of Dalian Maritime University, 2012, 38 (01): 9-11.
Zhen R, Jin Y, Hu Q, et al. Vessel behavior prediction based on AIS data and BP neural network [J]. Navigation of China, 2017, 40 (02): 6-10.
Wang G. Short-term prediction and simulation of ship’s motion based on LSTM [D]. Jiangsu University of Science and Technology, 2017.
Fan G, Sun R C, Shao F J, et al. Bus arrival time prediction based on LSTM and Kalman filtering [J]. Computer Applications and software, 2018, 35 (4): 91-96.
Li Y F, Cao H. Prediction for Tourism Flow based on LSTM Neural Network [J]. Procedia Computer Science, 2018, 129: 277-283.
Zhang J, Zhu Y, Zhang X, et al. Developing a Long Short-Term Memory (LSTM) based Model for Predicting Water Table Depth in Agricultural Areas [J]. Journal of Hydrology, 2018.
Yuan Y, Zhao Y, Wang Q. Action recognition using spatial-optical data organization and sequential learning framework [J]. Neurocomputing, 2018.
Huang X, Sun J, Sun J. A car-following model considering asymmetric driving behavior based on long short-term memory neural networks [J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 346-362.
PERERA L P, OLIVERIA P, SOARES C G. Maritime Traffic Monitoring Based on Vessel Detection Tracking State Estimation and Trajectory Prediction [J]. IEEE Transaction on Intelligent Transportation Systems, 2013, 13 (3): 1188-1200.
Feng Y, Zhao X, Han M, et al. The study of identification of fishing vessel behavior based on VMS data [C] //Proceedings of the 3rd International Conference on Telecommunications and Communication Engineering. 2019: 63-68.
Sekhon J, Fleming C. A Spatially and Temporally Attentive Joint Trajectory Prediction Framework for Modeling Vessel Intent [J]. arXiv preprint arXiv: 1912.09429, 2019.
Zhang Z, Yin J, Wang N, et al. Vessel traffic flow analysis and prediction by an improved PSO-BP mechanism based on AIS data [J]. Evolving Systems, 2019, 10 (3): 397-407.
Halafawi M, Stan M, Avram L. PREDICTION MODELING FOR PLATFORMS'NETWORK VESSELS PERFORMANCE [J]. Petroleum & Coal, 2019, 61 (5).
Yuan Z, Liu J, Liu Y, et al. A Novel Approach for Vessel Trajectory Reconstruction Using AIS Data [C] //The 29th International Ocean and Polar Engineering Conference. International Society of Offshore and Polar Engineers, 2019.
Kim J S, Lee J S, Kim K I. Anomalous Vessel Behavior Detection Based on SVR Seaway Model [J]. International Journal of Fuzzy Logic and Intelligent Systems, 2019, 19 (1): 18-27.
Graser A, Schmidt J, Dragaschnig M, et al. Data-driven Trajectory Prediction and Spatial Variability of Prediction Performance in Mari-time Location Based Services [C] //15th International Conference on Location-Based Services. 2019: 129.
Zissis D, Xidias E K, Lekkas D. Real-time vessel behavior prediction [J]. Evolving Systems, 2016, 7 (1): 29-40.
Hexeberg S, Flåten A L, Brekke E F. AIS-based vessel trajectory prediction [C] //2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017: 1-8.
HiFleet [EB/OL]. http://www.hifleet.com.
Gao, Miao & Shi, Guoyou & Li, Shuang. Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network [J]. Sensors. 2018, 18. 4211. 10.3390/s18124211.
Alvarellos A, Figuero A, Sande J, et al. Deep Learning Based Ship Movement Prediction System Architecture [C] //International Work-Conference on Artificial Neural Networks. Springer, Cham, 2019: 844-855.
Last P, Hering-Bertram M, Linsen L. Interactive History-based Vessel Movement Prediction [J]. IEEE Intelligent Systems, 2019.
Virjonen P, Nevalainen P, Pahikkala T, et al. Ship movement prediction using k-NN method [C] //2018 Baltic Geodetic Congress (BGC Geomatics). IEEE, 2018: 304-309.
Kim K I, Lee K M. Deep learning-based caution area traffic prediction with automatic identification system sensor data [J]. Sensors, 2018, 18 (9): 3172.