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

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.

• Record URL:
  • https://doi.org/10.1016/j.trd.2024.104060
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1361920924000178
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • © 2024 The Author(s). Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Chen, Bingkun
  • 0000-0001-5857-2798
  • Chen, Zhuo
  • Liu, Xiaoyue Cathy
  • Yi, Zhiyan
  • 0000-0002-2386-3883
• Publication Date: 2024-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 104060
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 127
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Bayes' theorem; Behavior; Drivers; Electric vehicle charging; Electric vehicles; Regression analysis
• Geographic Terms: Salt Lake City (Utah)
• Subject Areas: Energy; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01906556
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 31 2024 9:12AM