Abstract
In this paper, the chaotic characteristics of the railway passenger flow are considered, and the PSR-LSTM (Phase Space Reconstruction-Long Short Term Memory) model is proposed by the phase space reconstruction method to recover the hidden trajectory in the passenger flow. First, this model uses C-C method to calculate the time delay and embedding dimension, and carry out phase space reconstruction. Afterwards, the LSTM neural network is used to predict short-term passenger flow. In the experimental part, it is proved that the passenger flow data with chaotic characteristics are reconstructed by phase space processing can get more accurate predictions. Then, in order to further verify the accuracy of the model, this model is compared with the BP neural network model and the SVR model, which is also subjected to phase space reconstruction processing. The experimental results show that the model has high accuracy.
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
Huang Z, Feng S (2014) Grey forecasting model in the application of railway passenger flow prediction research. Technol Econ Areas Commun 16(1):57–60 (in Chinese)
Zhang B (2014) Study on short-term forecast of Shanghai-Nanjing intercity railway passenger flow. Chin Railways 2014(9):29–33 (in Chinese)
Dong S (2013) The research of short-time passenger flow forecasting based on improved BP neural network in urban rail transit. Beijing Jiaotong University (in Chinese)
Deng J, Kong F, Chen X (2008) Passenger flow forecast of urban rail transit based on support vector regression. J Chongqing Univ Sci Technol (Nat Sci Edn) 10(3):147–149 (in Chinese)
Takens F (2006) Determining strange attractors in turbulence. Lecture Notes Math 2006:366–381
Ma H, Li X, Wang G, Han C et al (2004) Selection of embedding dimension and delay time in phase space reconstruction. J Xi’an Jiaotong Univ 38(4):335–338 (in Chinese)
Kugiumtzis D (1998) State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length. Physica D-Nonlinear Phenomena 95(1):13–28
Kim H, Eykholt R, Salas J (1999) Nonlinear dynamics, delay times, and embedding windows. Elsevier Science Publishers B. V
Broock W, Scheinkman J, Dechert W et al (1996) A test for independence based on the correlation dimension. Econometric Rev 15(3):197–235
Hochreiter S, Schmidhuber J (2012) Long short-term memory. Neural Comput 9(8):1735–1780
Rosenstein M, Collins J, Luca C (1993) A practical method for calculating largest Lyapunov exponents from small data sets. Physica D-Nonlinear Phenomena 65(1–2):117–134
Acknowledgements
This work is supported by Railway Corporation Research Project (NO. 2016X005-B). Jiansheng Zhu is the corresponding author.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, Y., Zhu, J., Zhang, J. (2018). Short-term Passenger Flow Forecasting Based on Phase Space Reconstruction and LSTM. In: Jia, L., Qin, Y., Suo, J., Feng, J., Diao, L., An, M. (eds) Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017. EITRT 2017. Lecture Notes in Electrical Engineering, vol 482. Springer, Singapore. https://doi.org/10.1007/978-981-10-7986-3_69
Download citation
DOI: https://doi.org/10.1007/978-981-10-7986-3_69
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7985-6
Online ISBN: 978-981-10-7986-3
eBook Packages: EnergyEnergy (R0)