Policy interventions and urban characteristics in modeling electric vehicle charging infrastructure utilization

Abstract

The surge in electric vehicles adoption necessitates understanding the impact of policy interventions on public electric vehicle charging infrastructure in urban areas. This research investigates the influence of pricing frameworks on the usage of public charging facilities by analyzing both behavioral and spatial attributes of these infrastructures. Utilizing open data from Palo Alto, United States, this study employs descriptive statistical methods and interpretable machine learning approaches to scrutinize the relationship between policy initiatives and charging behaviors. The analysis underscores the significance of spatial attributes on charging behaviors. Policy interventions yield noticeable alterations in charging metrics, with locations near commercial hubs showing higher utilization, while local and frequent users resist fee adjustments. The research emphasizes the necessity for customized strategies to optimize infrastructure development and management, offering a framework for policymakers and stakeholders in sustainable urban transportation. Future research should explore similar interventions in diverse urban settings using real-time data and advanced optimization techniques to better tailor policies to the unique characteristics of specific facilities.