Abstract
Exploring the influencing factors of intercity travel mode choice can reveal passengers’ travel decision mechanisms and help traffic departments to develop an effective demand management policy. To investigate these factors, a survey was conducted in Xi’an, China, to collect data about passengers’ travel chains, including airplane, high-speed railway (HSR), train, and express bus. A Bayesian mixed multinomial logit model is developed to identify significant factors and explicate unobserved heterogeneity across observations. The effect of significant factors on intercity travel mode choice is quantitatively assessed by the odds ratio (OR) technique. The results show that the Bayesian mixed multinomial logit model outperforms the traditional Bayesian multinomial logit model, indicating that accommodating the unobserved heterogeneity across observations can improve the model fit. The model estimation results show that ticket purchasing method, comfort, punctuality, and access time are random parameters that have heterogeneous effects on intercity travel mode choice.
Highlights
Traditional discrete modeling methods such as the multinomial logit model assume that the effect of each factor is fixed for all individuals
We explored the influence mechanism of competitive factors for intercity modes and found unobserved heterogeneity of significant factors in the intercity model choice
A mixed multinomial logit model was established to explore the relevant factors of intercity mode choice, which was compared with the Bayesian multinomial logit model
Summary
Traditional discrete modeling methods such as the multinomial logit model assume that the effect of each factor is fixed for all individuals. The potential interaction between individual characteristics and other factors resulted in an unobserved heterogeneous effect on travel mode choices [17, 18]. E Bayesian mixed multinomial logit model is applied to the intercity mode choice to overcome the above defects. E objectives of this study are to (a) explore significant factors affecting passengers’ intercity travel mode choices from the perspective of the entire travel chain and (b) examine the unobserved heterogeneous effects of significant factors under a Bayesian framework. Passengers’ activity data over the whole process of intercity travel were collected in the city of Xi’an, China, and a Bayesian mixed multinomial logit model was employed to explore significant factors and their heterogeneous effects. Passengers’ activity data over the whole process of intercity travel were collected in the city of Xi’an, China, and a Bayesian mixed multinomial logit model was employed to explore significant factors and their heterogeneous effects. e effect of significantly competitive factors among modes (i.e., airplane, HSR, train, and express bus) was assessed by the odds ratio
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