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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.

has been cited by the following article:

• TITLE: The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method

  AUTHORS: Qian Li, Yong Qin, Ziyang Wang, Zhongxin Zhao, Minghui Zhan, Yu Liu, Zhiguo Li

  KEYWORDS: Urban Rail Transit; Neural Network; Sectional Passenger Flow; Prediction Method

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.4, November 12, 2013

  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.