A shared parking optimization framework based on dynamic resource allocation and path planning

Xie, Minghui; Zhang, Xinying; Wu, Zhouhao; Wei, Sen; Gao, Yanan; Wang, Yuanqing

doi:10.1016/j.physa.2023.128649

Cited by 8 publications

(1 citation statement)

References 52 publications

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“…Zhang and Liu considered operator's income and expenditure in the parking allocation models [24,25]. Xue et al further considered the impact of rejecting requests on the platform and added the penalty fee into their objective functions to avoid high rejection rate [26][27][28]. To reduce cruising trafc, Gao et al integrated shared parking into the ride-sourcing platform and proposed a platform proft maximization novel business model [29].…”

Section: Introductionmentioning

confidence: 99%

Optimal Parking Slots Reservation and Allocation Problem for Periodic Parking Platforms with Preference Constraints

Song,

Zhan,

et al. 2023

Journal of Advanced Transportation

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Various solutions, such as parking reservation systems, have been proposed to alleviate the difficulty in finding parking slots. In such systems, parking requests are submitted in advance by drivers, and the systems will reserve appropriate parking spots for drivers if their requests are accepted. However, the parking slots may be allocated unreasonably, which may lead to a waste of space and time resources. In addition, there is a game relationship between operator’s profit (OP) and users’ benefits (UB), which may affect the sustainable development of the system, if balanced improperly. Given the drivers’ arrival and departure time and their parking preference, the paper proposes a periodic reservation and allocation mode (PRAM) and establishes a dual-objective binary integer linear model to solve the reservation and allocation problem. The model aims to maximize the comprehensive benefits of the operator and users and to take full advantage of parking resources. We proposed a TOPSIS-SA algorithm (Technique for Order Preference by Similarity to an Ideal Solution and Simulated Annealing algorithm) to solve our model. Numerical experiments show that our model performs better than the baseline models on all performance metrics such as total operating profit, users’ average walking distance, acceptance rate, and utilization of parking slots.

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