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Vessel Traffic Flow Prediction Using LSTM Encoder-Decoder

Published: 29 October 2022 Publication History

Abstract

Accurate vessel traffic flow prediction is of vital indispensable for the enhancement of capability of navigation, the optimal allocation of port resources and the improvement of the navigation safety. In order to improve the accuracy of vessel traffic flow prediction, a prediction approach based on Long Short-Term Memory Encoder-Decoder (LSTM-ED) is proposed for multi-step prediction of vessel traffic flow. In order to study vessel traffic flow, a statistical approach of vessel traffic flow is proposed by using the data of Automatic Identification System (AIS). The vessel traffic flow data of the Liuhe Waterway in the Jiangsu section of the Yangtze River is selected as the experimental data for model training, validating and testing. Build the LSTM-ED-based prediction model and verify its validity in predicting vessel traffic flow. Experimental results show that the proposed prediction approach can accurately predict the trend of vessel traffic flow, the LSTM-ED-based approach can obtain better prediction performance compared with other baseline methods.

References

[1]
Toyoda, S., & Fujii, Y. (1971). Marine traffic engineering. The Journal of Navigation, 24(1), 24-34. https://doi.org/10.1017/S0373463300047755
[2]
H He, W., Zhong, C., Sotelo, M. A., Chu, X., Liu, X., & Li, Z. (2019). Short-term vessel traffic flow forecasting by using an improved Kalman model. Cluster Computing, 22(4), 7907-7916. https://doi.org/10.1007/s10586-017-1491-2
[3]
Wang, C., Zhang, X., Chen, X., Li, R., & Li, G. (2017, July). Vessel traffic flow forecasting based on BP neural network and residual analysis. In 2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS) (pp. 350-354). IEEE. https://doi.org/10.1109/iccss.2017.8091438
[4]
Xu, H., & Jiang, C. (2019). Deep belief network-based support vector regression method for traffic flow forecasting. Neural Computing and Applications, 32, 2027-2036. https://doi.org/10.1007/s00521-019-04339-x
[5]
Lu, S., Zhang, Q., Chen, G., & Seng, D. (2021). A combined method for short-term traffic flow prediction based on recurrent neural network. Alexandria Engineering Journal, 60(1), 87-94. https://doi.org/10.1016/j.aej.2020.06.008
[6]
Kumar, S. V., & Vanajakshi, L. (2015). Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 7(3), 1-9. https://doi.org/10.1007/s12544-015-0170-8
[7]
Li, M. W., Han, D. F., & Wang, W. L. (2015). Vessel traffic flow forecasting by RSVR with chaotic cloud simulated annealing genetic algorithm and KPCA. Neurocomputing, 157, 243-255. https://doi.org/10.1016/j.neucom.2015.01.010
[8]
Zhang, L., Liu, Q., Yang, W., Wei, N., & Dong, D. (2013). An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction. Procedia - Social and Behavioral Sciences, 96, 653–662. https://doi.org/10.1016/j.sbspro.2013.08.076
[9]
Schimbinschi, F., Moreira-Matias, L., Nguyen, V. X., & Bailey, J. (2017). Topology-regularized universal vector autoregression for traffic forecasting in large urban areas. Expert Systems with Applications, 82, 301–316. https://doi.org/10.1016/j.eswa.2017.04.015
[10]
Zhao, J., & Sun, S. (2016). High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems, 17(7), 2014–2019. https://doi.org/10.1109/tits.2016.2515105
[11]
Han, X. U. E., Zheping, S. H. A. O., Jiacai, P. A. N., & Feng, Z. H. A. N. G. (2020). Vessel traffic flow prediction based on CFA-GRNN algorithm. Journal of Shanghai Jiaotong University, 54(4), 421-429. https://doi.org/10.16183/j.cnki.jsjtu.2020.04.011
[12]
Wang, X., Li, J., & Zhang, T. (2019). A machine-learning model for zonal ship flow prediction using AIS data: A case study in the south atlantic states region. Journal of Marine Science and Engineering, 7(12), 463. https://doi.org/
[13]
Xu, X., Bai, X. E., Xiao, Y., He, J., Xu, Y., & Ren, H. (2021). A Port Ship Flow Prediction Model Based on the Automatic Identification System and Gated Recurrent Units. Journal of Marine Science and Application, 20(3), 572-580. https://doi.org/10.1007/s11804-021-00228-9
[14]
Zhang, Z. G., Yin, J. C., Wang, N. N., & Hui, Z. G. (2019). Vessel traffic flow analysis and prediction by an improved PSO-BP mechanism based on AIS data. Evolving Systems, 10(3), 397-407. https://doi.org/10.1007/s12530-018-9243-y
[15]
Tian, Y., Zhang, K., Li, J., Lin, X., & Yang, B. (2018). LSTM-based traffic flow prediction with missing data. Neurocomputing, 318, 297-305. https://doi.org/10.1016/j.neucom.2018.08.067
[16]
Mariet, Z., & Kuznetsov, V. (2019, April). Foundations of sequence-to-sequence modeling for time series. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 408-417). PMLR. https://doi.org/10.48550/arXiv.1805.03714
[17]
Wang, Z., Su, X., & Ding, Z. (2020). Long-term traffic prediction based on lstm encoder-decoder architecture. IEEE Transactions on Intelligent Transportation Systems, 22(10), 6561-6571. https://doi.org/10.1109/TITS.2020.2995546
[18]
Park, S. H., Kim, B., Kang, C. M., Chung, C. C., & Choi, J. W. (2018, June). Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. In 2018 IEEE Intelligent Vehicles Symposium (IV) (pp. 1672-1678). IEEE. https://doi.org/10.1109/ivs.2018.8500658
[19]
Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., & Huang, G.-B. (2018). Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1559–1582. https://doi.org/10.1109/tits.2017.2724551
[20]
Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In The 32nd International Conference on International Conference on Machine Learning (pp. 448-456). PMLR. https://doi.org/10.21203/rs.3.rs-1027530/v1
[21]
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation, 31(7), 1235-1270. https://doi.org/10.1162/neco_a_01199
[22]
Brownlee, J. (2017). Long short-term memory networks with python: develop sequence prediction models with deep learning. Machine Learning Mastery.

Cited By

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  • (2024)A Review on Motion Prediction for Intelligent Ship NavigationJournal of Marine Science and Engineering10.3390/jmse1201010712:1(107)Online publication date: 5-Jan-2024
  • (2024)Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity groupingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107012126:PBOnline publication date: 1-Feb-2024
  • (2024)A transformer-based method for vessel traffic flow forecastingGeoInformatica10.1007/s10707-024-00521-z29:1(149-173)Online publication date: 30-May-2024

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    cover image ACM Other conferences
    SPML '22: Proceedings of the 2022 5th International Conference on Signal Processing and Machine Learning
    August 2022
    309 pages
    ISBN:9781450396912
    DOI:10.1145/3556384
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 29 October 2022

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    View all
    • (2024)A Review on Motion Prediction for Intelligent Ship NavigationJournal of Marine Science and Engineering10.3390/jmse1201010712:1(107)Online publication date: 5-Jan-2024
    • (2024)Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity groupingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107012126:PBOnline publication date: 1-Feb-2024
    • (2024)A transformer-based method for vessel traffic flow forecastingGeoInformatica10.1007/s10707-024-00521-z29:1(149-173)Online publication date: 30-May-2024

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