A Novel Approach to Identifying Electric Vehicle Users via Mobile Phone Location Data and Spatial Analysis

Keywords: Electric Vehicles, Users, Mobile Phone Data, Spatial Analysis
Abstract Type: Paper Abstract

Abstract

Identifying electric vehicle (EV) users is crucial for optimizing charging infrastructure, improving transportation planning, and gaining insights into mobility behaviors. Traditional methods of accessing EV user data, such as surveys and GPS data from EVs, often have significant limitations. These methods typically capture smaller populations, are costly to implement, and raise privacy concerns, making them less suitable for large-scale studies. This study pioneers the use of mobile phone location data, which provides a vast, more comprehensive, and anonymized dataset, for detecting EV users. By leveraging the extensive nature of mobile location data, this research offers a cost-effective and scalable alternative that marks a significant advancement in the field of EV user detection.
Our methodology incorporates spatial features of EV charging station locations and applies advanced pattern recognition techniques to detect oscillations, identify repeated visits to charging stations, and differentiate EV users from conventional vehicle users. Specific criteria such as frequent presence at EV charging stations, staying in charging locations for a minimum duration, and visiting several times in a month are used to accurately discern EV users.
The results demonstrate the effectiveness and feasibility of using mobile phone location data for identifying EV users. This pioneering approach not only enables more efficient optimization of charging station placement but also offers a powerful tool for studying EV charging behaviors at a broader scale. The insights gained from this study can be applied to enhance energy consumption management, improve EV infrastructure, and support future research on sustainable transportation systems.