Exploring influential factors of fleet and parking management in shared autonomous vehicle systems: An agent-based simulation framework

Abstract

Shared autonomous vehicle (SAV) is expected to enhance urban transportation efficiency through innovative mobility resource management. By developing a comprehensive agent-based simulation framework, this study investigates several key factors influencing fleet size and parking demand for adoption of SAVs in future urban mobility systems. The framework examines the joint impact of both operational (e.g., reservation time and maximum waiting time) and demand-side characteristics (e.g., demand rate and origin-destination balance) on SAV system performance. A two-stage simulation procedure is developed. In the first stage, the ideal fleet size and parking demand are estimated such that the specified travel demand and service quality are satisfied. The derived service design is then implemented in the second stage to assess additional SAV system performance indicators including rejection rate and empty meters traveled. To obtain a holistic understanding of the studied factors, we construct various simulation scenarios based on historical taxi data in central areas of Chengdu, Shanghai (China) and Manhattan of New York City (USA), and build a regression model on the simulation outcomes. The results reveal a general mechanism by which operational characteristics and demand patterns influence SAV fleet and parking size, as well as system performance across cities with distinct structures and layouts. A balanced OD distribution shows a particularly significant impact on reducing fleet size, parking space, and empty meters. Several trade-offs such as the balance between fleet size and service quality are also identified, providing insights into the deployment of SAVs from the perspectives of both operators and regulators.

Keywords: Shared autonomous vehicles, fleet management, parking management, agent-based simulation, shared mobility