Bridging the Gap: Integrating Crowdsourced Data with Structured Analytics for Adaptive EV Charging Infrastructure

The growing popularity of Electric Vehicles (EVs) has created an urgent need for smarter and more adaptable charging infrastructure. Traditional datasets from fleet management systems provide useful insights into past usage patterns but fail to address the real-time factors that impact user experience and infrastructure efficiency. This study explores the integration of crowdsourced, citizen-generated data with traditional datasets to address these limitations. Through exploratory data analysis, we examine energy consumption patterns, charging behaviors, and infrastructure utilization across diverse vehicle types. Recognizing the limitations of existing data, we propose a framework that integrates fleet data with real-time user feedback to enable predictive analytics, dynamic station deployment, and enhanced user satisfaction. This approach highlights the potential of citizen science to improve the adaptability of EV charging systems and contributes to broader discussions on participatory data ecosystems, smart mobility solutions, and human-centered infrastructure planning.