Abstract
In the face of the development of the waterway collaboration transportation, the big data analysis of the waterborne transport has become a hot topic in the field of transportation. Due to the nonlinear and not obvious periodic space-time characteristics of ship traffic flow, it is a great challenge to accurately predict it. In this paper, a CNN-BiLSTM prediction model based on residual fitting network and attention mechanism is proposed, which considers the temporal and spatial characteristics of ship flow, and the extraction analysis of the ship big data and short-term ship flow prediction of sea port. The model extracts the multi-dimensional features of traffic with the help of one-dimensional convolution layer, obtains information mining on the forward and backward sequential data of the two-way long and short time memory network. Besides, the attention mechanism is used to enhance the learning of important features and residual fitting is used to enhance the fit of results. In this paper, we collected the AIS data in waters near New York Harbor, and the multi-segment regional traffic flow dataset is constructed by dividing regions. By comparison with ARIMA, LSTM, GRU, BiLSTM, CNN-LSTM and Attention-BiLSTM models, the proposed prediction model has higher prediction accuracy and a higher degree of fitting with the real value of ship flow.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Liu, R.W., Chen, J., Liu, Z.: Vessel traffic flow separation-prediction using low-rank and sparse decomposition. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE (2017)
Liu, Y., Yu, H., Fang, H.: Application of KNN prediction model in urban traffic flow prediction. In: 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT), pp. 389–392. Haikou, China (2021)
Kumar, V., Vanajakshi, L.: Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur. Transp. Res. Rev. 7(3), 1–9 (2015)
Liu, C.Y., Chen, S.Z.: Prediction of vessel traffic flow based on grey Markov model. Navig. China 41(3), 98–103 (2018)
Huang, Q.F.: Research on Forecasting Method Based on Wavelet Transform and Neural Network. Xiamen University, Xiamen (2020)
Wang, C., Zhang. X., Chen. X.: 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 (2017)
Feng, H., Kong, F., Xiao, Y.: Vessel traffic flow forecasting model study based on support vector machine. In: Shen, G., Huang, X. (eds.) ECWAC 2011. CCIS, vol. 143, pp. 446–451. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20367-1_72
Zhang, Z., Yin, J., Wang, N.: Vessel traffic flow analysis and prediction by an improved PSO-BP mechanism based on AIS data. Evol. Syst. 10(3), 397–407 (2019)
Hongren, J.: A random forest model based on parameter optimization using cuckoo search algorithm for ship traffic flow forecasting. In: Proceedings of the 34th Chinese Control and Decision Conference (CCDC), pp. 4960–4964 (2022)
Ma, X.L., Tao, Z.M., Wang, Y.H.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C: Emerg. Technol. 54, 187–197 (2015)
Ji, Z., Wang, L., Zhang, X.B.: Ship traffic flow forecast of Qingdao port based on LSTM. In: 2021 6th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), vol. 12081, pp.1208124-1–1208124-9 (2022)
Yan, Z., Yu, C.C., Han, L.: Short-term traffic flow prediction method based on CNN+LSTM. Comput. Eng. Design 40(9), 2620–2624 (2019)
Liu, Y.P., Zheng, H.F., Feng, X.X.: Short-term traffic flow prediction with Conv-LSTM. In: 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 23–31. IEEE (2017)
Suo, Y.F., Chen, W.K., Yang, S.H.: Prediction of ship traffic flow based on deep neural network. J. Jimei Univ. (Natural Science) 25(6), 430–436 (2020)
Yang, B.L., Sun, S.L., Li, J.Y.: Traffic flow prediction using LSTM with feature enhancement. Neurocomputing 332, 320–327 (2019)
Su, W.J., Liu, M.J.: Short term ship flow prediction based on whale algorithm optimized attention-BiLSTM model. J. Wuhan Univ. Technol. 44(05), 34–39 (2022)
Liu, J.X., Gao, G.X.: Short-term water traffic flow prediction with convolutional neural network and long short-term memory network. Navig. China 45(2), 56–61 (2022)
MarineCadastre.gov. https://marinecadastre.gov/accessais/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, T., Gai, X., Liu, S., Gao, S., Ouyang, M., Chen, L. (2024). Vessel Traffic Flow Prediction and Analysis Based on Ship Big Data. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_16
Download citation
DOI: https://doi.org/10.1007/978-981-99-9637-7_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9636-0
Online ISBN: 978-981-99-9637-7
eBook Packages: Computer ScienceComputer Science (R0)
