Citation

Dou F, Jia L, Wang L, Xu J, Huang Y. Comput. Intell. Neurosci. 2014; 2014: e950371.

Affiliation

School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.

Copyright

(Copyright © 2014, Hindawi Publishing)

DOI

10.1155/2014/950371

PMID

25431586

PMCID

PMC4238178

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.

Language: en