  This paper investigates the influencing factors in passengers' multimodal
traffic choice behaviors and provides a decision-making basis and improvement
strategies. By collecting large individual-level data through a comprehensive
field survey that was carried out at the major transportation hubs in Xian City
in China from the 1st to the 10th of March 2018, we compared 21 variables from
the data with four travel modes including air, high-speed rail (HSR),
traditional passenger train, and express bus. Among the variables, 12 variables
were used as the independent variables after the correlation analysis and the
collinearity test, including age, car ownership, ticketing method, travel
purpose, travel distance, fare rate, inter-city travel time per hundred
kilometers, safety, comfort, punctuality, access time, and departure time. The
regression relationships between the travel mode choice and the independent
variables were studied using Bayesian binomial logistic regression (BBL) and
Bayesian multiple logistic regression (BMNL). The receiver operating
characteristic (ROC) curve was applied to analyze the predictive performance of
the regression models. The results show that the area under curve (AUC) of the
ROC curves for the air travel, high-speed train, passenger train, and express
bus using BMNL were 0.9785, 0.9263, 0.8614, and 0.9025, respectively. The AUC
value of the ROC curve of the express bus using BBL was 0.8797, which indicated
that the passenger travel choice models established by Bayesian logistic
regressions had good prediction ability. The application of Bayesian logistic
regression in traffic mode choice is beneficial to enriching the assessments of
mode-choice behaviors, and the findings of this study can provide a basis for
formulating traffic management strategies in practice for shareholders and
policymakers.
