Matching Supply and Demand in Online Parking Reservation Platforms
Authors: Karaliopoulos, M., Mastakas, O. and Chai, W.K.
Journal: IEEE Transactions on Intelligent Transportation Systems
Volume: 24
Issue: 3
Pages: 3182-3193
eISSN: 1558-0016
ISSN: 1524-9050
DOI: 10.1109/TITS.2022.3230087
Abstract:Our work concerns online parking reservation platforms proposed in the last decade to cope with the parking challenge in cities worldwide. Enlisting parking resources from commercial operators (e.g., lots) and individuals (e.g., doorways) and letting drivers make online reservations through mobile apps, those platforms seek to ease transactions between the two sides and best match parking supply with parking demand. This way they maximize their value for drivers and parking space providers but also their revenue out of charged commissions. We distinguish between two types of commissions these platforms typically charge, fixed per transaction and proportional to its value, and formulate the respective optimization problems for the platform revenue maximization. We show that the two problems are NP-hard and design a novel algorithm that can treat both by combining greedy and dynamic programming principles. We study its optimality properties both analytically and experimentally, showing that the algorithm closely tracks optimal solutions for small and moderate problem sizes at run times that are several orders of size smaller than those needed by off-the-shelf ILP solvers. We then analyze real parking data we collected for the period 2018-2020 from the Bournemouth city in UK to realistically model the rich spatiotemporal dynamics of parking demand such as the location, start times and duration of parking events. These datasets drive the experimental evaluation of the proposed algorithm, which reports gains of up to 35% compared to the de facto parking resource reservation policy in such platforms. Notably, the highest gains are achieved when the platform operates under constrained supply conditions.
https://eprints.bournemouth.ac.uk/37828/
Source: Scopus
Matching Supply and Demand in Online Parking Reservation Platforms
Authors: Karaliopoulos, M., Mastakas, O. and Chai, W.K.
Journal: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume: 24
Issue: 3
Pages: 3182-3193
eISSN: 1558-0016
ISSN: 1524-9050
DOI: 10.1109/TITS.2022.3230087
https://eprints.bournemouth.ac.uk/37828/
Source: Web of Science (Lite)
Matching supply and demand in online parking reservation platforms
Authors: Karaliopoulos, M., Mastakas, O. and Chai, W.K.
Journal: IEEE Transactions on Intelligent Transportation Systems
Publisher: IEEE
ISSN: 1524-9050
https://eprints.bournemouth.ac.uk/37828/
Source: Manual
Matching supply and demand in online parking reservation platforms.
Authors: Karaliopoulos, M., Mastakas, O. and Chai, W.K.
Journal: IEEE Transactions on Intelligent Transportation Systems
Volume: 24
Issue: 3
Pages: 3182-3193
Publisher: IEEE
ISSN: 1524-9050
Abstract:Our work concerns online parking reservation platforms proposed in the last decade to cope with the parking challenge in cities worldwide. Enlisting parking resources from commercial operators (e.g., lots) and individuals (e.g., doorways) and letting drivers make online reservations through mobile apps, the goal of those platforms is to ease transactions between the two sides and best match parking supply with parking demand. This way they maximize their value for drivers and parking space providers but also their revenue out of charged commissions. We distinguish between two types of commissions these platforms typically charge, fixed per transaction and proportional to its value, and formulate the respective optimization problems for the platform revenue maximization. We show that the two problems are NP-hard and design a novel algorithm that can treat both, combining greedy and dynamic programming principles. We study its optimality properties both analytically and experimentally, showing that the algorithm closely tracks optimal solutions for small and moderate problem sizes at run times that are several orders of size smaller than off-the-shelf ILP solvers. We then analyze real parking data we collected for the period 2018-2020 from the Bournemouth city in UK to realistically model the rich spatiotemporal dynamics of parking demand such as the location, start times and duration of parking events. These data drive the experimental evaluation of the proposed algorithm, which reports gains of up to 35% compared to the de facto reservation policy in use in such platforms. Notably, these gains are higher when the platform operates under constrained supply conditions.
https://eprints.bournemouth.ac.uk/37828/
Source: BURO EPrints