Accidents are inevitable in the transportation systems; however, harnessing the big data generated from traffic accidents can significantly enhance the intelligence of the transportation system. Multiple unmanned aerial vehicles (multi-UAVs), owing to its flexibility and collaboration, can rapidly track the accidents and gather the acquire relevant data with reasonable deployment. Therefore, we propose an architecture utilizing multiple UAVs to track traffic accidents (targets) and collect relevant data. However, dynamic factors such as no-fly zones and reappearing targets in real-world traffic environments pose challenges to the rapid deployment of UAVs with current algorithms. In this paper, we design a model for deploying multiple UAVs, considering no-fly zones and dynamically reappearing targets. This model aims to optimize UAV deployment by minimizing both the flight distance of each UAV and the associated risk. Risk is defined as the urgency of processing accident scenes, which escalates with the elapsed time from the initial appearance of the targets to their processing. To address the joint UAV deployment problem, we first introduce an algorithm termed preprocessing and group-crossover nondominated sort genetic algorithm II (PGC-NSGAII). In PGC-NSGAII, we employ a group-based selection crossover (GBSC) method to enhance the algorithm's search capability. This method segregates the initial population into two groups, selecting two individuals for crossover within each group. Secondly, building upon the framework of NSGAII, we incorporate a novel preprocessing component to enrich solution diversity. Thirdly, we develop a prediction method for dynamic environment parameters and a no-fly zone avoidance strategy for multi-UAV deployment. Finally, our experimental results demonstrate that PGC-NSGAII surpasses other existing methods in scenarios with varying numbers of UAVs or targets. Compared with state-of-the-art optimization methods, PGC-NSGAII proves to be more efficient in UAV deployment in dynamic environment.
Sun et al. (2024) studied this question.