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Optimal Guidance Algorithms for Parking Search with Reservations

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Listed:
  • Michael W. Levin

    (University of Minnesota)

  • Stephen D. Boyles

    (The University of Texas at Austin)

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.

Suggested Citation

  • Michael W. Levin & Stephen D. Boyles, 2020. "Optimal Guidance Algorithms for Parking Search with Reservations," Networks and Spatial Economics, Springer, vol. 20(1), pages 19-45, March.
    • Handle: RePEc:kap:netspa:v:20:y:2020:i:1:d:10.1007_s11067-019-09464-7
    DOI: 10.1007/s11067-019-09464-7
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    References listed on IDEAS

    as
    1. Kaspi, Mor & Raviv, Tal & Tzur, Michal & Galili, Hila, 2016. "Regulating vehicle sharing systems through parking reservation policies: Analysis and performance bounds," European Journal of Operational Research, Elsevier, vol. 251(3), pages 969-987.
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    3. Liu, Yang & Nie, Yu (Marco), 2011. "Morning commute problem considering route choice, user heterogeneity and alternative system optima," Transportation Research Part B: Methodological, Elsevier, vol. 45(4), pages 619-642.
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    5. Boyles, Stephen D. & Tang, Shoupeng & Unnikrishnan, Avinash, 2015. "Parking search equilibrium on a network," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 390-409.
    6. He, Fang & Yin, Yafeng & Chen, Zhibin & Zhou, Jing, 2015. "Pricing of parking games with atomic players," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 1-12.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Legros, Benjamin & Fransoo, Jan C., 2024. "Admission and pricing optimization of on-street parking with delivery bays," European Journal of Operational Research, Elsevier, vol. 312(1), pages 138-149.
    2. Xie, Minghui & Zhang, Xinying & Wu, Zhouhao & Wei, Sen & Gao, Yanan & Wang, Yuanqing, 2023. "A shared parking optimization framework based on dynamic resource allocation and path planning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    3. Benjamin Legros & Jan C. Fransoo, 2024. "Admission and Pricing Optimization of On-Street Parking with Delivery Bays," Post-Print hal-04432404, HAL.
    4. Xinliu Sui & Xiaofei Ye & Tao Wang & Xingchen Yan & Jun Chen & Bin Ran, 2022. "Microscopic Simulating the Impact of Cruising for Parking on Traffic Efficiency and Emission with Parking-and-Visit Test Data," IJERPH, MDPI, vol. 19(15), pages 1-26, July.
    5. Legros, Benjamin & Fransoo, Jan C., 2023. "Admission and pricing optimization of on-street parking with delivery bays," Other publications TiSEM 6d41ee5c-27dc-4d34-aff1-4, Tilburg University, School of Economics and Management.

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