基于交通指数聚类的路网区域动态划分  

Dynamic division of road network areas based on traffic index clustering

作　　者：侯军军 龙佰超 王洪钰 肖建力[1] HOU Junjun;LONG Baichao;WANG Hongyu;XIAO Jianli(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《上海理工大学学报》2021年第4期360-367,共8页Journal of University of Shanghai For Science and Technology

基　　金：国家自然科学基金资助项目(61603257)。

摘　　要：针对宏观路网区域交通状态预报需要首先产生路网区域的需求,提出了一种新的基于交通指数聚类的路网区域动态划分方法。首先对整个城市路网进行网格化划分,将路段划分为从属于某个网格的子路段;然后,计算每个网格的交通指数,提取网格特征,从而得到样本特征矩阵;接着,利用k-means++聚类算法对样本特征矩阵进行聚类,得到初始聚类标签,并对其中奇异网格的聚类标签加以修正;最后,得到划分后的路网区域。为了验证该方法的性能,利用上海市的GPS数据对上海市进行了路网区域的划分,并与不同聚类方法的结果进行了对比。结果表明,新方法对路网区域划分的精度及稳定性均有所提高。As road network areas need to be generated firstly for traffic state forecasting of macro road network areas,a new dynamic division method of road network areas based on traffic index clustering was presented.The entire city′s road networks were first divided into grids,in which each road section belonged to one certain grid.Following by this,the traffic index for each grid was computed.Then,features for each grid were extracted to get sample feature matrix.The kmeans++clustering algorithm was used to cluster the sample feature matrix.Consequently,the initial clustering labels were generated.For better clustering results,the grid labels with singularity were modified.Finally,the completed road network areas were obtained.In order to verify the performance of the proposed method,the GPS data of Shanghai was utilized to divide road network areas,and the results of the proposed method were compared with the results obtained by other clustering methods.Experimental results show that the proposed method has improved the division accuracy and stability.

关 键 词：路网区域 动态划分 交通指数 k-means++聚类算法 

分 类 号：U491[交通运输工程—交通运输规划与管理]