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Title Enhancing The Smart Parking Assignment System Through Constraints Optimization
ID_Doc 24029
Authors Elkhalidi N.; Benabbou F.; Sael N.
Year 2024
Published IAES International Journal of Artificial Intelligence, 13, 2
DOI http://dx.doi.org/10.11591/ijai.v13.i2.pp2374-2385
Abstract Traffic in big cities has become a black spot for drivers. One of the major concerns is the parking problem that hinders urban mobility, particularly in big cities and other congested areas. This leads to an increase in accidents, a big consumption of fuel, and a spectacular augmentation of pollution. In this paper, we introduce a parking assignment system grounded in constraint programming to address the growing demand for efficient parking management in smart cities. Our system is designed to meet the requirements of groups of drivers seeking to reserve parking spaces simultaneously within the same period and geographical area. This entails imposing constraints on the desired parking type, including considerations such as walking and driving distances, parking costs, and availability. Within the scope of this study, we propose two formulations: constraint satisfaction programming (CSP) with an objective function and mixed-integer linear programming (MILP). Evaluation shows Choco, a CSP solver, is effective for smaller requests but slower for larger ones, while MILP excels for larger scenarios. Both solvers produce high-quality solutions meeting real-time response requirements. Our research offers innovative solutions for smart city management, considering parking type preferences, costs, and availability. We contribute significantly to parking space assignment methodologies, aiming to alleviate the time-consuming search for parking, reduce accidents, fuel consumption, and pollution. © 2024, Institute of Advanced Engineering and Science. All rights reserved.
Author Keywords Assignment problem; Constrained optimization; Management system; Multi-agent system; Smart parking


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