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Comput Intell Neurosci. 2014;2014:950371. doi: 10.1155/2014/950371. Epub 2014 Nov 5.

Fuzzy temporal logic based railway passenger flow forecast model.

Author information

1
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China ; Subway Operation Technology Centre, Mass Transit Railway Operation Corporation LTD, Beijing 102208, China.
2
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.
3
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China ; State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.
4
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.

Abstract

Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.

PMID:
25431586
PMCID:
PMC4238178
DOI:
10.1155/2014/950371
[Indexed for MEDLINE]
Free PMC Article

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