Bayesian inference-based spatiotemporal modeling with interim activities for EV charging etiquette
研究了插电式电动汽车司机中途活动与超时占用充电桩行为的关系，提出结合地理加权回归和贝叶斯推断的框架，无需调查数据即可分析时空因素影响。
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.