Assessment of Multimodal Choice Behavior for Domestic Passengers Using Bayesian Logistic Regressions: A Case Study in China

TL;DR

This study uses Bayesian logistic regressions on survey data from Chinese passengers to accurately model and predict multimodal travel choices, providing insights for transportation planning and policy.

Contribution

It introduces Bayesian logistic regression models for analyzing multimodal travel behavior, demonstrating high predictive accuracy and practical implications for traffic management strategies.

Findings

Bayesian models achieved high AUC scores, indicating strong predictive performance.

Key factors influencing mode choice include travel distance, fare, safety, and comfort.

Models can inform transportation policy and improve multimodal system planning.

Abstract

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.