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.

插电式电动汽车（PEV）驾驶员不良的充电礼仪，例如拔掉其他电动汽车的插头以及电动汽车充满电后逾期停留，将造成充电资源的服务瓶颈，甚至阻碍电动汽车的普及。为了探索 PEV 驾驶员的临时活动与逾期逗留行为之间的潜在联系，本研究引入了一种创新框架，该框架通过专用的基于活动的贝叶斯推理模块实施地理和时间加权回归 (GTWR)。具体来说，充电期间出行选择建模的贝叶斯推理模块很好地解决了 PEV 驾驶员出行行为的随机性。随后，根据预测的旅行选择和预期活动持续时间构建 GTWR 模型，以捕获逾期居留和活动特征之间的时空关联。整个建模框架进一步应用于犹他州盐湖城的案例研究，并在没有调查数据的情况下推理时空因素的影响方面表现出卓越的适应性。