Research ArticleOpen AccessGoogle Scholar indexed
The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method
The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
Beijing Rail Transit Network Management Co. Ltd., Beijing, China.
Beijing Rail Transit Network Management Co. Ltd., Beijing, China.
Commercial Department of the Australian Consulate-General Guangzhou, Guangzhou, China.
- 1 The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
- 2 The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
- 3 The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
- 4 The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
- 5 Beijing Rail Transit Network Management Co. Ltd., Beijing, China.
- 6 Beijing Rail Transit Network Management Co. Ltd., Beijing, China.
- 7 Commercial Department of the Australian Consulate-General Guangzhou, Guangzhou, China.
Journal of Intelligent Learning Systems and Applications·Volume 05 (2013)·Pages 227–231·Published 12 November 2013·DOI10.4236/jilsa.2013.54026
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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.
KeywordsUrban Rail TransitNeural NetworkSectional Passenger FlowPrediction Method
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