Parking Pricing and Design in the Morning Commute Problem with Regular and Autonomous Vehicles

Autonomous vehicles can profoundly change parking behavior in the future. Instead of searching for parking, autonomous vehicle owners get dropped off at their final destination and send their occupant-free cars to a parking spot. In this paper, we study the impact of parking in a bi-class morning commute problem with autonomous and regular vehicles. We consider a spatial distribution of parking spaces, which allows us to capture the parking location of travelers. In the equilibrium condition, autonomous vehicles leave home later (than regular vehicles), and park farther away from their destination. The regular vehicle travelers, however, leave home sooner to park closer to the destination with a smaller walking distance. The reverse occurs if regular travelers abdicate walking and take a faster mode from their parking space to the city center. To optimize the system, we develop temporal and spatial parking pricing strategies and a new parking supply design scheme, as practical alternatives for the conventional dynamic congestion pricing. The proposed parking pricing strategies incentivize commuters to adjust their travel schedules by charging a parking price that increases with distance from the city center. Hence, those who park closer to their destination have to pay less. This is a counter-intuitive finding at first which arises from a trade-off with the earliness penalty of users who park close to the destination. We show that system optimality is also reached by redistributing the parking supply. Our numerical experiments capture the impact the autonomous vehicle penetration rate on the performance of the system and the proposed parking pricing and design strategies.

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