Toward Understanding the Impact of User Participation in Autonomous Ridesharing Systems

TL;DR

This paper uses simulation analysis to quantify how user participation impacts autonomous ridesharing system efficiency and provides insights for optimizing system configurations based on data-driven tradeoffs.

Contribution

First simulation study quantifying the effect of user participation on ARS performance and identifying optimal system configurations to mitigate efficiency loss.

Findings

User participation significantly affects ARS efficiency.

System configurations can be optimized to counter performance loss.

Data-driven simulations inform tradeoffs between efficiency and user behavior.

Abstract

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.