Parking choice behaviour analysis of rural residents based on the latent variable random forest model

The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution, which has attracted the attention of many scholars as well as policymakers. However, most of the current research on parking choice focuses on urban business and residential areas rather than on rural parking choice behaviour, and focuses on the analysis of observable factors, ignoring the internal relationship with potential variables. This study considers the heterogeneity of individuals and uses the random forest (RF) algorithm to construct a model of rural residents' willingness to choose parking with both latent and explicit variables, to explore how much and in what ways individual characteristics and parking characteristics affect rural residents' parking choices, and to explore parking planning programmes and strategies that are truly applicable to rural areas. The results of the study suggest that the safety and convenience of the parking environment are key factors influencing the parking choice behaviour of rural residents, and can greatly improve the predictive accuracy of the parking willingness model. Upon comparison, it is found that the application of the RF algorithm is also significantly better than the logit model in terms of prediction effect, indicating that there is a nonlinear effect among the factors influencing the parking choice behaviour of rural residents and that the RF model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behaviour of rural residents.