Deployment and optimization of electric taxi vehicles in urban areas

Abstract

Electric vehicles (EVs) are at the forefront of the transition toward sustainable urban transportation. Among them, electric taxis have emerged as an eco-friendly al- ternative to traditional internal combustion engine vehicles, offering reduced emissions and operational noise. However, integrating electric taxis into urban mobility systems introduces new challenges—ranging from limited battery capacities to inadequate char- ging infrastructure and complex service optimization needs. This thesis addresses these challenges through the development of an intelligent decision-making framework based on Deep Q-Network (DQN) reinforcement learning. We investigate methods to optimize taxi dispatching, customer service, and charging behavior while minimizing downtime and maximizing system efficiency. The proposed system architecture integrates a realistic sim- ulation environment with performance evaluation metrics tailored to electric taxi services. A comparative analysis with existing optimization methods highlights the strengths and limitations of various approaches. Experimental results demonstrate the efficacy of our proposed solution in improving energy efficiency, customer service rate, and operational sustainability. This work offers practical insights for deploying intelligent EV taxi systems and contributes to the broader goal of sustainable smart city development.