Matching Supply and Demand in Online Parking Reservation Platforms

Our work concerns online parking reservation plat-forms 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 dura-tion 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 reser-vation policy in such platforms. Notably, the highest gains are achieved when the platform operates under constrained supply conditions.

Automated summary

Our work focuses on online parking reservation platforms that have been proposed in the past decade to address the parking challenges in cities worldwide. These platforms aim to facilitate transactions between parking space providers and drivers by enlisting parking resources from commercial operators and individuals, allowing drivers to make online reservations through mobile apps. We formulate optimization problems for maximizing the platform's revenue by differentiating between fixed per transaction and proportional commissions. We design a novel algorithm that combines greedy and dynamic programming principles to solve these NP-hard problems efficiently. Through experiments and analysis of real parking data, we demonstrate the algorithm's effectiveness in optimizing parking resource reservation, achieving gains of up to 35% compared to existing policies.