Optimizing Long-Distance Electric Vehicle Routes Based on Passenger Satisfaction Model

This study presents a novel approach to optimizing long-distance electric vehicle (EV) routes by prioritizing passenger happiness—a vital yet often overlooked aspect in route planning for sustainable transportation. Traditional EV route optimization models focus primarily on technical considerations such as energy efficiency, charging station availability, and minimizing travel time, yet they rarely account for the human-centric factors that shape travel satisfaction. This research introduces a comprehensive framework that integrates qualitative aspects of the travel experience, including scenic route preferences, comfort during travel, and enriching activities at charging stops. The employed methodology combines data analytics and psychological assessment to develop an EV route optimization model that aligns technical efficiency with passenger well-being. Computational experiments conducted across varied travel scenarios reveal that routes optimized for passenger happiness not only enhance the overall travel experience but also demonstrate potential to encourage broader EV adoption for long-distance journeys. The results underscore the importance of balancing technical efficiency with human-centric factors in EV route planning and highlight critical areas for infrastructure improvements, such as the strategic placement of high-power charging stations.