Abstract
The sharp increase in railway passenger flow during the Spring Festival Travel Season has tested the organization and dispatching ability of the railway transportation system. In this paper, the advantages of least square support vector machine (LSSVM) in small sample data prediction are integrated, and the ARIMA-LSSVM hybrid model based on residual linear transfer superposition is proposed, which is verified by Xiamen Spring Festival railway passenger flow. The analysis results show that the average absolute errors of hybrid model are 0.565 × 104 and 0.979 × 104 person times, respectively, which are 22.50% and 12.43% higher than ARIMA model, and 28.30% and 18.35% higher than LSSVM model. This study plays a positive role in improving the railway passenger flow forecasting ability and adjusting the preparation time during the Spring Festival Travel Season.
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
References
Wei, Y., Chen, M.C.: Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp. Res. Part C Emerg. Technol. 21(1), 148–162 (2012)
Zhang, F., Wu, T.-Y., Pan, J.-S., Ding, G., Li, Z.: Human motion recognition based on SVM in VR art media interaction environment. HCIS 9, 40 (2019)
Wu, Q., Zang, B.-Y., Zhang, Y., Qi, Z.-X.: Wavelet kernel twin support vector machine. J. Inf. Hiding Multimedia Signal Process. 12(2), 93–101 (2021)
Cortes, C., Mohri, M., Riley, M., et al.: Sample selection bias correction theory. In: Proceedings of the 19th International Conference on Algorithmic Learning Theory. Springer, Berlin, Heidelberg (2008)
Guo, S.: Prediction of short-term passenger flow on a bus stop based on LSSVM. J. Wuhan Univ. Technol. (Transportation Science & Engineering (2013)
Chen, C.-M., Chen, L., Gan, W., Qiu, L., Ding, W.: Discovering high utility-occupancy patterns from uncertain data. Inf. Sci. 546, 1208–1229 (2021)
Gan, W., Chen, L., Wan, S., Chen, J., Chen, C.-M.: Anomaly rule detection in sequence data. IEEE Trans. Knowl. Data Eng. (2021). https://doi.org/10.1109/TKDE.2021.3139086
Meng, P., Li, X., Jia, H., et al.: Short-time rail transit passenger flow prediction based on wavelet threshold denoising and support vector machine. Revista de la Facultad de Ingenieria 32(4), 292–302 (2017)
Yang, H.U.I., Yonggang, W.A.N.G., Hui, P.E.N.G., et al.: Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics. J. Traffic Transp. Eng. 21(04), 210–222 (2021)
Sun, Y., Leng, B., Guan, W.: A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166, 109–121 (2015)
Liu, S.Y., Liu, S., Tian, Y., et al.: Research on forecast of rail traffic flow based on ARIMA model. J. Phys: Conf. Ser. 1792(1), 012065 (2021)
Gu, D.-X., Chen, et al.: Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl. Soft Comput. (2015)
Acknowledgements
This project is supported by the Natural Science Foundation of Fujian Province No. 2021J011202, the Education and Research Project of Young and Middle-Aged Teachers of Fujian Province No. JAT190661, the Science and Technology Project of High-Level Talents of Xiamen City No. YKJ19021R, and the Graduate Science and Technology Innovation Project of Xiamen University of Technology No. YKJCX2021028.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, ZC., Chen, D., Jiang, PZ. (2023). Modeling and Analysis of Railway Passenger Flow Forecast During the Spring Festival. In: Ni, S., Wu, TY., Geng, J., Chu, SC., Tsihrintzis, G.A. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Smart Innovation, Systems and Technologies, vol 347. Springer, Singapore. https://doi.org/10.1007/978-981-99-0848-6_2
Download citation
DOI: https://doi.org/10.1007/978-981-99-0848-6_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0847-9
Online ISBN: 978-981-99-0848-6
eBook Packages: EngineeringEngineering (R0)