Assessing the impacts of fleet sizing and matching strategies for Shared Automated Vehicles (SAVs) in mixed mobility systems using an agent-based simulation approach

Abstract

Due to the challenges of traffic congestion, pollution, and low transportation efficiency in urban areas, Shared Automated Vehicles (SAVs) are considered a solution that can integrate into existing transportation frameworks to improve traffic performance and environmental sustainability. Currently, there is a research gap in managing the fleet size of SAVs under different matching strategies. Although the literature on the operational and environmental benefits of SAVs is growing, few studies systematically analyze how different fleet management strategies affect service levels and road congestion. This thesis fills this gap by exploring the impacts of Immediate Decision and Batch Offer strategies on fleet size and their corresponding effects on urban traffic performance.

The thesis focuses on the integration of SAV with mixed mobility systems, using SAVs to entirely replace current Mobility on Demand (MoD) services, employing an agent-based simulation method to study the impacts of fleet size and matching strategies on urban traffic performance. This research employs a case study approach, selecting the Haidian District in Beijing as the research area. It uses an innovative demand generation model based on travel distances as simulation request inputs. The model utilizes travel distance distribution functions based on historical data, road network information, and taxi GPS data to generate traffic demand based on network nodes through traffic zone division, probability density calculation, and travel distribution. The model employs the K-means clustering algorithm to divide traffic areas based on the density of traffic nodes rather than specific road network conditions, a macroscopic, low-resolution method with lower computational complexity suitable for large-scale road networks.

The mode choice model in this study analyzes how passengers make decisions between different modes of travel. The model, using a discrete choice framework, studies how passengers choose between SAVs(Shared Automated Vehicles) and POVs(privately operated vehicles). Employing the Logit model, the study considers several key factors influencing travel choice, including travel time, comfort, reliability, and environmental impact, to provide mode selection in simulations. This part emphasizes the importance of travelers' subjective perceptions.

The application of simulation and the Macroscopic Fundamental Diagram (MFD) are central methodologies of this study. In the simulation analysis part, this research uses the agent-based simulation tool FleetPy, focusing on evaluating two key matching strategies: Immediate Decision Simulation (IDS) and Batch Offer Simulation (BOS). By simulating different fleet sizes and matching strategies, it explores the dispatching and operational performance of Shared Autonomous Vehicles in urban road networks and compares them with traditional taxi systems.

The findings indicate that replacing taxi systems with an SAV system can significantly improve fleet efficiency, achieving the same service level with a smaller fleet size. In both small and large fleet scenarios, the SAV system demonstrates higher operational efficiency. In scenarios with smaller fleet sizes, the Batch Offer (BOS) strategy provides higher service levels. By batch processing and optimizing multiple requests, it can effectively improve the success rate of matching and reduce waiting times, while Immediate Decision (IDS) performs better in terms of service timeliness. Simulation results also show that as the fleet size increases, the advantages of the IDS strategy become apparent but may lead to wastage of vehicle resources.