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Article

Optimization of Electric Vehicle Charging Station Locations Based on Community Detection and User Usage Patterns

커뮤니티 디텍션과 사용자 이용 패턴을 반영한 전기차 충전소 입지 최적화

Hyunseok OH1
,
Jooyong LEE2*
오 현석1
,
이 주용2*
1Master Course, Department of Urban & Transportation Engineering, College of Creative Engineering, Kyonggi University, Gyeonggi 16227, Korea
2Assistant Professor, Department of Urban & Transportation Engineering, College of Creative Engineering, Kyonggi University, Gyeonggi 16227, Korea
1경기대학교 도시·교통공학과 석사과정
2경기대학교 스마트시티공학부 도시·교통전공 조교수
*Corresponding Author
30 June 2025. pp. 282-296
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Abstract

The number of registered electric vehicles (EVs) is increasing nationwide, highlighting the inseparable relationship between EVs and their charging infrastructure. While South Korea's charging infrastructure is considered relatively adequate, most charging stations are equipped with slow chargers and are predominantly located in public facilities where securing land is easier. However, this approach does not fully address EV users' accessibility needs. This study aims to identify the optimal locations for EV charging stations in Suwon City, Gyeonggi-do, by focusing on improving user accessibility. The research utilizes the Louvain algorithm for community detection, logistic regression analysis, and the Maximal Covering Location Problem (MCLP) model. The Louvain algorithm was applied to identify community structures based on Suwon’s estimated traffic volume, while logistic regression analysis was used to determine key factors influencing the placement of fast and slow charging stations, including weekday and weekend charging behaviors, station density, and highway interchange accessibility. These factors were translated into demand weights and incorporated into the MCLP model to optimize charging station placement. The results demonstrate that the optimal locations vary depending on budget conditions and provide enhanced accessibility compared to existing infrastructure. This study offers policy insights for the effective placement of EV charging stations, emphasizing the need for data-driven approaches to integrate charging stations with transportation networks and improve user accessibility.

Keywords
charging stations
electric vehicle
logistic regression
louvain algorithm
MCLP

전국적으로 전기차 등록 대수는 증가하는 추세이며, 전기차와 전기차 충전 인프라는 상호 불가분의 관계를 가진다. 국내에 충전 인프라의 보급은 양호한 수준으로 평가되었으나, 대부분의 충전소가 완속 충전소로 구성되어 있고 부지확보가 용이한 공공시설 위주로 설치되어 있어 전기차 사용자의 접근성이 충분히 고려되지 못했다는 한계가 있다. 본 연구는 경기도 수원시를 대상으로 전기차 충전소의 최적 입지를 선정하는 문제를 다루며, 전기차 사용자의 접근성을 반영한 최적의 충전소 위치를 찾는 것을 목표로 한다. 이를 위해 Louvain 알고리즘, 로지스틱 회귀분석 그리고 MCLP(Maximal Covering Location Problem) 모델을 이용하여 최적 입지를 도출하였다. 커뮤니티 디텍션 기법 중 Louvain 알고리즘을 활용하여 수원시 추정 교통량을 반영한 커뮤니티 구조를 도출하였으며, 로지스틱 회귀분석을 통해 급속 및 완속 충전소 설치에 영향을 미치는 주요 요인을 개별적으로 분석하였다. 분석된 주요 요인으로는 평일과 주말의 충전 행태, 충전소 밀도, 고속도로 IC 접근성이 포함되었으며, 이를 기반으로 충전소 입지 확률을 정량화하여 MCLP의 수요지 가중치로 활용하였다. 분석 결과, 충전소의 최적 입지는 예산 조건에 따라 다양한 범위의 커버리지를 제공할 수 있는 것으로 나타났다. 또한, Louvain 알고리즘을 적용한 최적 입지 모델은 기존 충전소 분포와 차별화된 결과를 보였으며, 이는 전기차 사용자의 접근성을 향상시키고 충전소 수요를 충족하는 데 기여할 수 있다. 본 연구는 전기차 충전소 최적 입지와 관련된 정책적 시사점을 제시하며, 전기차 사용자의 접근성을 높이고 교통 네트워크와의 연계를 고려한 최적 위치 선정의 중요성을 제시한다.

키워드
충전소
전기차
로지스틱 회귀분석
Louvain 알고리즘
MCLP
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 43
  • No :3
  • Pages :282-296
  • Received Date : 2024-12-03
  • Revised Date : 2025-01-08
  • Accepted Date : 2025-04-06
  • DOI :https://doi.org/10.7470/jkst.2025.43.3.282
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