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期刊信息
  • 主管单位:
  • 上海市教育委员会
  • 主办单位:
  • 上海理工大学
  • 主  编:
  • 庄松林
  • 地  址:
  • 上海市军工路516号
  • 邮政编码:
  • 200093
  • 联系电话:
  • 021-55277251
  • 电子邮件:
  • xbzrb@usst.edu.cn
  • 国际标准刊号:
  • 1007-6735
  • 国内统一刊号:
  • 31-1739/T
  • 邮发代号:
  • 4-401
  • 单  价:
  • 15.00
  • 定  价:
  • 90.00
侯军军,龙佰超,王洪钰,肖建力.基于交通指数聚类的路网区域动态划分[J].上海理工大学学报,2021,43(4):360-367.
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
投稿时间:2020-11-06  
DOI:10.13255/j.cnki.jusst.20201106001
中文关键词:  路网区域  动态划分  交通指数  k-means++聚类算法
英文关键词:road network areas  dynamic division  traffic index  k-means++ clustering algorithm
基金项目:国家自然科学基金资助项目(61603257)
作者单位E-mail
侯军军 上海理工大学 光电信息与计算机工程学院上海 200093  
龙佰超 上海理工大学 光电信息与计算机工程学院上海 200093  
王洪钰 上海理工大学 光电信息与计算机工程学院上海 200093  
肖建力 上海理工大学 光电信息与计算机工程学院上海 200093 audyxiao@sjtu.edu.cn 
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中文摘要:
      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。
英文摘要:
      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 k-means++ 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.
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