Can Autonomous Vehicles Solve the Commuter Parking Problem?

• 成果类型: Article; Early Access
• 署名作者: Mirzaeian, Neda; Cho, Soo-Haeng; Qian, Sean
• 署名单位: University of Texas System; University of Texas Dallas; Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.01213
• 发表日期: 2026
• 关键词: autonomous vehicles; congestion; optimal control; parking; Smart City Operations; transportation

摘要

This paper investigates how autonomous vehicles (AVs) may reshape morning commute travel patterns and impact parking in a central business district (CBD). We develop a continuous-time game-theoretic traffic model that takes into account key economic deterrents to driving, such as parking fees and traffic congestion, and characterize the departure time and parking location (inside or outside the CBD parking area) patterns of commuters in equilibrium. Our analysis shows that all AV commuters may choose to park outside the CBD in equilibrium, increasing both vehicle hours and vehicle miles traveled as compared with the case with all human-driven vehicles. This change increases the total system cost and suggests a potential for CBD land use changes (e.g., repurposing CBD parking spots as commercial and residential areas) after the mass adoption of AVs. To reduce the total system cost, an urban planner may regulate commuters' decisions by adjusting parking fees and/or imposing congestion tolls as a short-term measure, or adjusting infrastructure, for example, converting CBD parking spaces to drop-off spots for AVs. Our results indicate that these measures can reduce the total system cost substantially (e.g., up to 28:5% when calibrating our model to data from Pittsburgh).