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  
[PERSON]  
1 Asia Pacific School of Logistics, Graduate School of Logistics, Inha University,  
Incheon 2212, Republic of Korea  
Department of Urban Planning & Real Estate, Gangneung-Wonju National University,  
Gangneung 25457, Republic of Korea  
Department of Urban Planning & Real Estate, Gangneung-Wonju National University,  
Gangneung 25457, Republic of Korea  
demand at charging stations [8; 9; 10]. These research efforts have been effective in comprehending EV user charging patterns, suggesting that the charging demand varies with time and space [11; 12]. Therefore, the dynamic charging behavior of EV users should be considered to determine the optimal charging locations. Numerous studies conducted in various regions worldwide have accumulated evidence to help understand EV charging patterns. However, limited attention has been paid to the understanding of the heterogeneous charging behavior of EV users in different regions (e.g., a comparative analysis of charging patterns between different regions). A better understanding of the driving factors that influence different charging behaviors across regions can help policymakers construct effective charging infrastructure in regional contexts. Traditional modeling approaches that attempt to identify the factors influencing EV charging demand can be classified into three broad categories, depending on data availability: (1) statistical modeling based on stated or revealed preference surveys [13; 14; 15; 16; 17], (2) theoretical or mathematical simulation modeling [18; 19; 20; 21], and (3) data-driven statistical or machine learning modeling [12; 22; 23]. Early studies generally adopted a survey-based statistical modeling approach to estimate the charging demand based on reported survey responses. This approach has been effective in identifying the socioeconomic and built-environment factors that affect EV users' travel behaviors and the consequent charging demand. However, these studies have some limitations, as they relied on survey data based on EV use in hypothetical situations (especially in the stated preference survey), or the surveys typically relied on relatively small sample sizes because of cost and time constraints, which may not reflect the behaviors of the overall population in the study region. Theoretical or mathematical simulation modeling has been effective in investigating EV charging trends under various scenarios; however, the modeling methodologies proposed in the studies have not been fully validated under real-world conditions. Recent studies have attempted to model EV charging demand based on actual charging history data. This modeling approach was able to represent the actual charging patterns and overall showed a good performance in identifying the spatial and temporal variability of charging demand in the study regions. However, the empirical research results accumulated through the data-driven modeling approach are still insufficient, and this approach has limitations in identifying the various factors affecting the charging demand owing to difficulties in collecting related datasets. This study aims to fill this research gap in the literature by adopting a data-driven modeling approach that accounts for the factors influencing EV charging demand. To this end, we analyzed the historical data of the EV charging transactions of each EV charger at stations in Korea. Subsequently, we developed statistical models that account for the charging demand along with the influencing factors that explain the charging demand. We developed models for two study regions at different phases of the EV program: Jeju-do (maturity phase of the EV program) and Gangneung-si (early phase of the EV program), in Korea (Figure 1a).