Assess the Performance of Electric Autonomous Taxi System Using a Data-Driven Simulation Model

This paper presents a data-driven simulation model to estimate the potential of replacing conventional taxis with electric autonomous vehicles (EAVs). Vehicle trajectory data collected by onboard global positioning system (GPS) units in Shanghai, China, are used to study taxi travel patterns, in terms of the distribution of taxi travel demand, idle time between two consecutive occupied trips, and the places where vehicle stock imbalances may occur. The operational performances and fleet size are quantified using a data-driven simulation model that stimulates EAV taxis’ charging, idling and relocating activities. It is found that EAV taxis can serve the same amount of travel demand using 69.4% of the current fleet size; while because of vehicle stock imbalances, relocating idling vehicles is inevitable. Simulation results also demonstrate that the deployment of EAVs can improve taxi operation efficiency in terms of increasing the proportion of occupied travel distance.