Can Autonomous Vehicles Solve the Commuter Parking Problem?

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).