Sustainable operational strategies for mixed fleets: Integrating autonomous and human-driven taxis with heterogeneous energy types

Abstract

Taxi systems are transitioning into a complex integration of autonomous and human-driven vehicles powered by heterogeneous energy sources. Traditional operational strategies designed for homogeneous fleets fail to capture the unique dynamics and interactions present in mixed fleets. To address this gap, this study proposes a comprehensive modeling and simulation framework for the dynamic operation of mixed taxi fleets, including autonomous electric taxis (AETs), human-driven electric taxis, and human-driven gasoline taxis. The framework integrates centralized and decentralized control mechanisms to address the distinct characteristics of each taxi type. An integer linear programming model is developed to optimize taxi assignment and scheduling, with the objective of maximizing system profits by accounting for customer service revenues and energy and travel costs. An agent-based simulation platform is designed to model dynamic interactions among taxis, customers, and charging stations, offering continuous feedback on system performance. Real-world case studies reveal significant environmental, economic, and social benefits when incorporating operating costs into decision-making. Impact analyses demonstrate the competitiveness of AETs in passenger service due to lower operating costs and enhanced environmental efficiency, with reduced carbon emission intensity per kilometer and per request. This study provides valuable insights for taxi platforms and policymakers in formulating strategies that promote sustainable urban mobility during the ongoing transition period.