Data-Driven Model for Identifying Factors Influencing Electric Vehicle Charging Demand: A Comparative Analysis of Early- and Maturity-Phases of Electric Vehicle Programs in Korea

Abstract

With increasing concerns about urban pollution, electric vehicles (EVs) have offered an alternative mode of transportation that reduces urban pollution levels. Previous studies have sought to identify the various factors influencing EV charging patterns to deploy an appropriate charging infrastructure. However, limited attention has been paid to the investigation of different charging patterns identified in different regions at different phases of the EV program. This study aims to fill this research gap in the literature by developing binary logistic models that account for the factors influencing charging demands in different regions of Korea, i.e., Jeju-do and Gangneung-si. To this end, we collected historical data on EV charging transactions in these study regions and analyzed them to evaluate the difference in charging demands. The developed models suggest that the charging demand varies with charger characteristics and charging time. Moreover, different charging patterns in different regions can be explained by the different travel behaviors of those who use EVs for different trip purposes. These findings provide an important implication suggesting that policymakers should consider a stepwise strategy to construct charging infrastructure at the appropriate scale and configuration, depending on the phase of the EV program.