Toward Understanding the Impact of User Participation in Autonomous Ridesharing Systems
For stakeholders of autonomous ridesharing systems, this study provides data-driven insights to balance system efficiency and price of anarchy.
This paper presents the first simulation analysis quantifying how user participation affects autonomous ridesharing system efficiency, showing that specific configurations can mitigate performance loss from uncoordinated user behavior.
Autonomous ridesharing systems (ARS) promise many societal and environmental benefits, including decreased accident rates, reduced energy consumption and pollutant emissions, and diminished land use for parking. To unleash ARS' potential, stakeholders must understand how the degree of passenger participation influences the ridesharing systems' efficiency. To date, however, a careful study that quantifies the impact of user participation on ARS' performance is missing. Here, we present the first simulation analysis to investigate how and to what extent user participation affects the efficiency of ARS. We demonstrate how specific configurations (e.g., fleet size, vehicle capacity, and the maximum waiting time) of a system can be identified to counter the performance loss due to users' uncoordinated behavior on ridesharing participation. Our results indicate that stakeholders of ARS should base decisions regarding system configurations on insights from data-driven simulations and make tradeoffs between system efficiency and price of anarchy for desired outcomes.