Integrating probabilistic tensor factorization with Bayesian supervised learning for dynamic ridesharing pattern analysis

In the era of transportation big data, the analysis of mobility patterns generally involves large quantities of datasets with high-dimensional variables recording individual travelers' activities and socio-economic attributes, bringing new challenges to researchers. Conventional regression-based models commonly require complex structures in depicting random or fixed effects with a considerable number of parameters to estimate, and state-of-the-art machine learning models are regarded as black-boxes that are not clear in interpreting the mechanism in human mobility. To overcome the challenges of capturing complex high-order relationships among variables of interest, this paper proposes a Bayesian supervised learning tensor factorization (BSTF) model for the classification of travel choices in the mobility pattern analysis. The BSTF model induces a hierarchical probabilistic structure between predictor variables and the dependent variable, which offers a nature supervised learning foundation via Bayesian inference. Latent class (LC) variables are considered in the BSTF model to discover hidden preferences/states among travelers associated with their mobility patterns. We apply the BSTF model to analyze passenger-side choice patterns between diverse service options on a ride-sourcing platform, drawing increasing attention during recent years. A case study with a real-world dynamic ridesharing dataset in Hangzhou, China, is conducted. Different cases of training sizes are utilized to fit the proposed BSTF model as well as some other state-of-the-art machine learning models. By identifying significant variables and derive their probabilistic relationship between service types (i.e., ridesharing, non-sharing, and taxi), the proposed BSTF model offers good performance in both classification accuracy and the interpretability of shared mobility.