Parking Space Matching and Path Planning Based on Wolf Feeding Decision Algorithm in Large Underground Garage

Abstract

As cities grow, the number of complex underground parking garages with multiple entrances and exits is increasing. Randomly assigning parking spaces can lead to longer wait times for car owners during the parking and retrieval process and underutilization of resources. Therefore, allocating suitable parking spaces to car owners is crucial. This article proposes solutions for matching parking spaces and planning optimal routes for car owners in large underground parking garages based on their demands for shortest time, lowest price, and overall optimization. To reduce space and time complexity, a new heuristic decision optimization algorithm called Wolf Pack Search Algorithm (WSA) is proposed, which can solve multi-objective problems. WSA reduces redundant searches by using existing information to guide the search process. WSA simulates a wolf pack searching for food to find the optimal time cost for each intersection, reducing the space complexity of the data. Through information exchange and cooperative search, the wolf pack can find the optimal solution, abandoning redundant data. Compared with the ant colony algorithm, WSA has a significant advantage in space complexity and can meet car owners’ requirements for parking space matching efficiency.