A scalable non-myopic atomic game for a smart parking mechanism

Hamid R. Sayarshad, Shahram Sattar and H. Oliver Gao

Transportation Research Part E: Logistics and Transportation Review, 2020, vol. 140, issue C

Abstract: We propose a novel non-myopic smart parking mechanism which aims to decrease the cruising time spent in searching for parking, with the assumption of elastic demand for both on-street parking lots and parking garages. A non-myopic atomic game is formulated to address competition for parking through assignment of vehicles to candidate parking facilities that takes into account the differences in travel times for the vehicles from their point of origin to the parking facilities and the differences in walking times for the drivers from the parking facilities to their final destination, as well as dynamic pricing, cruising times, and occupancies of the parking facilities. This study integrates a socially efficient price that accounts for the waiting times of drivers in their search for parking. We incorporate a game model into the social optimum problem by considering the competition of drivers for parking spaces where the drivers’ preferences are reflected in a collective decision such as social welfare. Using actual parking data for the city of San Francisco, we found that under our proposed dynamic parking system the average social welfare per vehicle improved by up to 54% compared to other parking strategies.

Keywords: Markov decision process; Dynamic pricing; Game theory; Social optimum; Non-myopic smart parking mechanism (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.tre.2020.101974

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