A train dispatching model based on fuzzy passenger demand forecasting during holidays

Fei Dou Dou, Jie Xu, Li Wang, Limin Jia



Purpose: The train dispatching is a crucial issue in the train operation adjustment when passenger flow outbursts. During holidays, the train dispatching is to meet passenger demand to the greatest extent, and ensure safety, speediness and punctuality of the train operation. In this paper, a fuzzy passenger demand forecasting model is put up, then a train dispatching optimization model is established based on passenger demand so as to evacuate stranded passengers effectively during holidays.

Design/methodology/approach: First, the complex features and regularity of passenger flow during holidays are analyzed, and then a fuzzy passenger demand forecasting model is put forward based on the fuzzy set theory and time series theory. Next, the bi-objective of the train dispatching optimization model is to minimize the total operation cost of the train dispatching and unserved passenger volume during holidays. Finally, the validity of this model is illustrated with a case concerned with the Beijing-Shanghai high-speed railway in China.

Findings: The case study shows that the fuzzy passenger demand forecasting model can predict outcomes more precisely than ARIMA model. Thus train dispatching optimization plan proves that a small number of trains are able to serve unserved passengers reasonably and effectively.

Originality/value: On the basis of the passenger demand predictive values, the train dispatching optimization model is established, which enables train dispatching to meet passenger demand in condition that passenger flow outbursts, so as to maximize passenger demand by offering the optimal operation plan.


railway transportation, passenger demand forecasting, train dispatching, fuzzy logical relationships, optimization model

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DOI: http://dx.doi.org/10.3926/jiem.699

Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2021

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience