JILSA  Vol.5 No.4 , November 2013
The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method
ABSTRACT
This paper studies the short-term prediction methods of sectional passenger flow, and selects BP neural network combined with the characteristics of sectional passenger flow itself. With a case study, we design three different schemes. We use Matlab to realize the prediction of the sectional passenger flow of the Beijing subway Line 2 and make comparative analysis. The empirical research shows that combining data characteristics of sectional passenger flow with the BP neural network have good prediction accuracy.

Cite this paper
Q. Li, Y. Qin, Z. Wang, Z. Zhao, M. Zhan, Y. Liu and Z. Li, "The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 4, 2013, pp. 227-231. doi: 10.4236/jilsa.2013.54026.
References
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[2]   J. Mao, “Urban Rail Transit Passenger Flow Short-Term Prediction Method and Empirical Research,” Beijing Jiaotong University, Beijing, 2012.

[3]   R. Yang, “Study on Passenger Flow Forecast and Operation Scheduling Method of Urban Rail Transit,” Beijing Jiaotong University, Beijing, 2010.

[4]   L. N. Wang, “Traffic Prediction and Scheduling of Urban Passenger Rail Network Based on Historical Data,” Beijing Jiaotong University, Beijing, 2011.

[5]   D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning Representations by Back-Propagation Errors,” Nature, Vol. 323, 1986, pp. 533-536.
http://dx.doi.org/10.1038/323533a0

[6]   P. Jiang, Q. Shi, W. W. Chen, et al., “Prediction of Passenger Volume Based on Elman Type Recurrent Neural Network,” Journal of Hefei University of Technology (Science), Vol. 31, No. 3, 2008, pp. 340-342, 369.

 
 
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