Optimal Guidance Algorithms for Parking Search with Reservations

Michael W. Levin () and Stephen D. Boyles ()
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Michael W. Levin: University of Minnesota
Stephen D. Boyles: The University of Texas at Austin

Networks and Spatial Economics, 2020, vol. 20, issue 1, No 2, 19-45

Abstract: Abstract To alleviate the congestion caused by searching for parking, internet- or smartphone-based parking reservation systems have been deployed in major cities. We develop algorithms to provide optimal guidance to individual drivers on where to search for or reserve parking and how to navigate the traffic while searching for parking, a problem which has yet to be addressed in the literature. Drivers holding a reservation pay a holding cost per unit time until they park, so it is often suboptimal to reserve parking before departing. We formulate a Markov decision process to decide both where to attempt to reserve parking and which route to take. The optimal parking space to reserve changes as the driver travels through the network. Results on the downtown Austin network show that reserving parking affects route choice and reduces cruising for parking compared to not reserving parking. Our model and solution algorithm could be integrated with GPS navigation systems to provide guidance to individual drivers on optimal navigation and use of parking reservation systems.

Keywords: Parking reservation search; Online shortest path; Markov decision process (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11067-019-09464-7

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