Encouraging car users who travel short distances to shift from car mode to active travel modes can effectively alleviate urban traffic congestion and reduce carbon emissions. However, few studies have examined the determinants of the travel mode choice of short-distance car users and ignored the nonlinear associations and interactions between variables. This paper conducts a questionnaire survey to investigate the short-distance travel mode choice of car users who travel less than 4 km in a specific city. A random forest (RF) model is applied to examine the influence of key variables on these three travel mode choices of short-distance car users and to explore the nonlinear associations and interactions of the variables. Compared with multinomial logic model, the results of RF show that significant threshold effects exist in the relationship between the car user’s travel mode choice and the selected explanatory variables, mainly travel distance, road network density, distance to CBD, and number of bus stops. In particular, 1.2 km is a critical turning point for car and active travel mode choice, before which car users prefer to travel by walking and cycling and after which there is a significant increase in the car use probability. When the road network density was between 2.5 km/km2 and 6.5 km/km2, the proportion of car users who chose cycling showed an increasing trend, while car use showed a decreasing trend. These findings can provide a solid basis for planning managers to develop policy measures to encourage environmentally sustainable travel.
Examining how travel distance is associated with travel mode choice is essential for understanding traveler travel patterns and the potential mechanisms of behavioral changes. Although existing studies have explored the effect of travel distance on travel mode choice, most overlook their non-linear relationship and the heterogeneity between groups. In this study, the correlation between travel distance and travel mode choice is explored by applying the random forest model based on resident travel survey data in Guiyang, China. The results show that travel distance is far more important than other determinants for understanding the mechanism of travel mode choice. Travel distance contributes to 42.28% of explanation power for predicting travel mode choice and even 63.24% for walking. Significant nonlinear associations and threshold effects are found between travel distance and travel mode choice, and such nonlinear associations vary significantly across different socioeconomic groups. Policymakers are recommended to understand the group heterogeneity of travel mode choice behavior and to make targeted interventions for different groups with different travel distances. These results can provide beneficial guidance for optimizing the spatial layout of transportation infrastructure and improving the operational efficiency of low-carbon transportation systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.