Exploring the determinants of public transport usage and shared mobilities: A case study from Nanchang, China

Abstract

• A first joint analysis of shared mobility comparing with public transport usage. • Markov Chain Monte Carlo (MCMC) and Bayes-Mixed Logit algorithm. • Safety issues have significant impact on reducing females’ car-pooling usage. Shared mobility became popularized in many urban cities as new sustainable transport pattern that provides multimodal and flexible mobility solutions. In order to derive a reasonably allocation for urban shared mobility resources and increase the use of shared mobility, this paper investigates urban residents’ travel behavior and the choice of shared mobility in urban area of a high-density population city in China, by considering the effect of individual psychological attributes, economy status, traffic environment of city network based on Markov Chain Monte Carlo (MCMC) and Bayes-Mixed Logit algorithm. The empirical results indicate that weather condition has a great impact on shared mobility usage, and travel time is significantly influencing residents’ willingness of choosing car-pooling service. The study also finds that safety issues have significant impact on reducing female population on the rate of car-pooling usage. The current work is an attempt to address gaps in exploring the determinants of public transport usage and shared mobilities, particularly when a comprehensive analysis of multiple factors is needed. This research provides implementable insights for further formulating efficient utilization of urban transport resources and rational regulations on shared mobility.