基于M2M模型的出行路径规划算法

Path planning algorithm based on M2M model

提出了一种源自于人类认知思维模式的普遍适用的算法设计模型——M2M模型(Macro to Micro model)。M2M模型的数据结构用O(n)时间建成,并具备高度的并行性。针对交通网络的最优路径规划问题,提出了基于M2M模型的交通网络出行路径规划算法,将交通路网运用M2M模型分层分块,减少搜索区域,优化路径搜索速度。实验证明,相对于当前使用较多的Dijkstra及A*等经典算法,本算法在求解速度上有明显提高。

A generally applicable algorithm named Macro-to-Micro Model(M2M)is introduced which is derived from the human thinking pattern.The M2M data structure can be built in O(n)time.It can also be built in O(1)time using parallel technology.The thesis proposes a new algorithm based on the M2M.It transform the transportation network to multi-lever and multi-part to decrease the search region.Experiments show that it improves the path search speed compared with the widely used Dijkstra and A* algorithms.