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Passengers’ Demand Characteristics Experimental Analysis of EMU Trains with Sleeping Cars in Northwest China

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  • Yuzhao Zhang

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Jianqiang Wang

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Wenjuan Cai

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

Passenger demand characteristics for electrical multiple unit (EMU) trains with sleeping cars will directly affect the train operation scheme in a long transportation corridor. Descriptive statistics of individual attributes and passenger choice intentions for EMU trains with sleeping cars are calculated based on the revealed preference (RP) and stated preference (SP) survey data in Northwest China to illustrate the overall conditions of passengers’ demands. Considering the higher dimensionality and multi-collinearity in the dataset of influencing factors, the factor analysis method was first adopted to reduce the number of dimensions of the raw dataset and obtain orthogonal common factors. Then, the ordinal logistic regression model was adopted to test and perform a regression analysis based on multinomial logit theory. The analysis shows that these influencing factors, such as income, profession, educational background and residence, would have a greater impact on the choice of an EMU train with sleeping cars. It is significant that passengers’ choice intentions are positively correlated with income and educational background. The result can provide some reference for the decision-making regarding operating an EMU train with sleeping cars in Northwest China. In addition, the proposed method can be applied to the analysis of passengers’ demand characteristics in similar situations.

Suggested Citation

  • Yuzhao Zhang & Jianqiang Wang & Wenjuan Cai, 2019. "Passengers’ Demand Characteristics Experimental Analysis of EMU Trains with Sleeping Cars in Northwest China," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5338-:d:271307
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