自适应粒度的道路移动对象聚类算法

Self-adaptable Granularity Road Network Moving Objects' Clustering Algorithm

以往的聚类算法能够减少道路交通网络中移动对象与中心数据库的通信开销,但聚类粒度的大小是根据经验设定的。分析了影响距离聚类粒度大小的因素,提出用BP网络来训练历史数据,动态地获取距离聚类粒度值和时间粒度值,并把这些粒度值作为新的历史数据来训练网络,使得粒度值能够根据道路交通网络中因素的改变而动态改变,从而产生有效的道路网络聚类,减少通信开销,并预报道路交通的拥堵情况,为最优路径规划提供依据。

Although previous clustering algorithms can reduce the communication cost between moving objects and central database in road traffic network,the clustering granularity is set by experiences.This paper analysed the influence factors on clustering distance granularity,and introduced a novel method to train historical data with BP network,and then got clustering distance granularity and clustering time granularity dynamically.Being new historical data,these granularity values can be made to train BP network further.This network can self-adapt in respect of influence factors dynamically,and birth efficient clustering granularity values to reduce communication cost,and forecast traffic jams as optimal route planning＇s observation.