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Addressing the practical requirements of multi-vehicle route planning and parking guidance within the context of intelligent transportation systems (ITS), this article proposes an improved path planning algorithm designed for ITS-based smart parking environments. Firstly, an enhanced comprehensive evaluation model is proposed under infeasible area constraints, incorporating multi-dimensional quantitative evaluations of distance, road condition, and parking space state. By considering the parking space partition constraint of the preceding vehicle, this model adjusts the parking space allocation strategy in real time, effectively reducing congestion and conflicts during the allocation process. Furthermore, a risk prediction model based on pheromone concentration is introduced to avoid path conflicts. By integrating this prediction model into the conflict-based search (CBS) method, a novel multi-vehicle path planning algorithm (pheromone-conflict-based search (P-CBS)) is proposed. This algorithm resolves path conflicts among multiple vehicles in parking lots and ensures that each vehicle can find a conflict-free path to its target parking spot. Simulation results show that, compared to the traditional CBS algorithm, the proposed P-CBS algorithm reduces computation time by 32.61% and decreases total path cost by 9.8% on average. These results demonstrate the efficiency, scalability, and real-time applicability of the proposed method for smart parking scenarios in ITS, providing a practical solution for intelligent vehicle coordination and parking management.
Yang et al. (Thu,) studied this question.