Bayesian inference-based spatiotemporal modeling with interim activities for EV charging etiquette

•This study addresses the overstaying issue from the activity perspective.•We refine the GTWR by enabling an activity-based Bayesian inference module.•The stochastic travel behaviors are incorporated in the Bayesian inference module.•The interconnections between overstaying and activities are captured in GTWR.•The entire modeling framework is evaluated in a real-world case study. Poor charging etiquette of Plug-in Electric vehicle (PEV) drivers, such as unplugging other PEVs and overstaying after the PEV is fully charged, will create a service bottleneck to charging resources and even impede PEV penetration. To explore the underlying linkage between PEV drivers’ interim activities and the behavior of overstaying, this study introduces an innovative framework that implements Geographically and Temporally Weighted Regression (GTWR) with a dedicated activity-based Bayesian inference module. Specifically, the stochasticity of PEV drivers’ travel behaviors is well addressed in the Bayesian inference module for travel choice modeling during charging sessions. Subsequently, the GTWR model is constructed based on predicted travel choices and expected durations of activities to capture the spatiotemporal interconnections between overstaying and activity characteristics. The entire modeling framework is further applied to a case study in Salt Lake City, Utah, and demonstrates superior adaptability in reasoning the impacts of spatiotemporal factors without survey data.