A Data-Driven Approach for Optimizing Early-Stage Electric Vehicle Charging Station Placement

This article presents a novel and practical data-driven approach to suboptimally allocate charging stations for electric vehicles (EVs) in an early-stage setting. Specifically, we investigate the following problem: For a city with a limited budget for public EV charging infrastructure construction, where should the charging stations be deployed in order to promote the transition of EVs from traditional cars? We develop a
$\delta$
-nearest model and a
$K$
-nearest model that can capture people's satisfaction toward a certain design and formulate the early-stage EV charging station placement problem as a monotone submodular maximization problem utilizing fine-grained population, trip, transportation network, and point of interest data. A greedy-based algorithm is proposed to solve the problem efficiently with a provable approximation ratio. A case study of Haikou is provided to demonstrate the effectiveness of our approach.