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针对交通诱导中的分布式诱导和中心式诱导各自的不足,提出了基于路网分层的协同式诱导算法。首先,根据出行偏好,对路网进行了分层,并对不同形式的路径进行了分析。然后,通过对子区域中路径搜索进行动态搜索限定,提出了基于改进A*的跨层节点确定方法,在此基础上建立了基于改进的跨层路径搜索算法。最后,构建了协同式诱导算法模型,此模型运用中心式诱导完成主干道路网层交通流的诱导,而分布式诱导完成子区域小范围次要路网上的车辆的路径搜索,并对协同搜索算法进行了仿真验证。结果表明:该算法模型相比单一诱导模型计算性能好,平均搜索的效率提高了17.5倍。
Aiming at the deficiency of distributed guidance and central guidance in traffic guidance, a cooperative guidance algorithm based on road network hierarchy is proposed. First, according to travel preferences, the road network has been stratified, and different forms of the path were analyzed. Then, based on the dynamic search limitation of the path search in the sub-region, a cross-layer node determination method based on the improved A * is proposed. Based on this, an improved cross-layer path search algorithm is established. Finally, a cooperative induction algorithm model is constructed. The model uses central guidance to guide the traffic flow in the main road network layer, and the distributed induction leads to the completion of the vehicle path search in the sub-area on the minor road network. The algorithm has been verified by simulation. The results show that compared with the single induced model, the proposed algorithm has better computational performance and the average search efficiency is improved by 17.5 times.