Comparative optimization of electric robo-taxi (eRT) and electric unmanned aerial vehicle (eUAV) systems

This study presents a comparative optimization framework for electric robo-taxi (eRT) and electric unmanned aerial vehicle (eUAV) systems under a unified set of design and operational constraints. The objective is to evaluate the trade-off between total system cost and target travel time, providing a quantitative basis for assessing the feasibility and efficiency of both urban mobility modes. The proposed framework integrates fleet sizing, charging infrastructure design, and battery capacity optimization. The results demonstrate the differences in optimal design and cost-performance characteristics between the eRT and eUAV systems across various target travel times. The eRT system is found to be more cost-effective due to its lower infrastructure requirements, whereas the eUAV system significantly reduces travel time, offering a compelling solution for fast urban transport. The study focuses on system-level comparative analysis, excluding aspects such as real-world calibration, passenger pooling, regulatory constraints, and user behavior modeling, which are reserved for future research. This work offers insights into the design trade-offs and operational feasibility of emerging electric mobility systems under common optimization conditions.