基于路网分层的协同诱导路径搜索算法

A Cooperative Guidance Path Searching Algorithm Based on Hierarchical Road Network

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 shortcomings of distributed guidance and central guidance in traffic guidance, a cooperative guidance algorithm based on hierarchical road network is put forward. First, according to the travel preference, the road network is layered, and the different forms of path are analysed. Second, a cross- layer path searching algorithm based on improved A ＊ is established on the basis of dynamic searching restriction in sub-domain and the confirmation of cross-layer node based on improved. Finally, the cooperative path guidance algorithm model is established, which implements the central guidance layer to complete the main road network＇s traffic flow guidance, while the distributed guidance completes sub regional road network＇s path search within a small scale. The performance of the cooperative searching algorithm is verified through simulation. The result shows that this algorithm model performance very well compared with a single guidance algorithm, and its efficiency would be multiplied 17.5 times.