Can Autonomous Vehicles Solve the Commuter Parking Problem?

55 Pages Posted: 8 Jul 2021

See all articles by Neda Mirzaeian

Neda Mirzaeian

Carnegie Mellon University

Soo-Haeng Cho

Carnegie Mellon University - Tepper School of Business

Sean Qian

Carnegie Mellon University

Date Written: June 22, 2021

Abstract

This paper investigates how autonomous vehicles (AVs) may change the morning commute travel pattern and improve downtown parking. We develop a continuous-time traffic model that takes into account key economic deterrents to driving, such as parking fee and traffic congestion, and characterize the departure time and parking location (downtown or outside downtown parking area) patterns of commuters in equilibrium. To illustrate our results, our model is calibrated to data from Pittsburgh. For the calibrated model, our analysis shows that all AV commuters choose to park outside downtown, increasing both vehicle hours and vehicle miles traveled as compared to the case with all human-driven vehicles. This change increases the total system cost and suggests a potential downtown land-use change (e.g., repurposing downtown parking spots to commercial and residential areas) in Pittsburgh after mass adoption of AVs. To reduce the total system cost, a social planner may be interested in regulating commuters’ decisions by adjusting parking fees and/or imposing congestion tolls as a short-term measure, or adjusting infrastructure, e.g., converting downtown parking spaces to curbside drop-off spots for AVs. Our results indicate that these measures can reduce the total system cost substantially (e.g., up to 70% in our calibrated model).

Keywords: Autonomous Vehicles, Congestion, Game Theory, Parking, Smart City Operations, Transportation

Suggested Citation

Mirzaeian, Neda and Cho, Soo-Haeng and Qian, Sean, Can Autonomous Vehicles Solve the Commuter Parking Problem? (June 22, 2021). Available at SSRN: https://ssrn.com/abstract=3872106 or http://dx.doi.org/10.2139/ssrn.3872106

Neda Mirzaeian

Carnegie Mellon University ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Soo-Haeng Cho (Contact Author)

Carnegie Mellon University - Tepper School of Business ( email )

Pittsburgh, PA 15213-3890
United States

Sean Qian

Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States

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