基于行车时间的道路网络时空自相关模型研究
来源期刊：中南大学学报(自然科学版)2012年第10期
论文作者：赵玲 王佳璆 邓敏 黄晶
文章页码：4114 - 4122
关键词：道路网络；交通流；行车时间；时空自相关分析；邻接矩阵
Key words：road network; traffic flow; journey time; spatial-temporal autocorrelation analysis; adjacency matrix
摘 要：以城市道路网络为研究对象，提出一种适用于道路网络的邻接矩阵建立方法，并建立一种基于邻接关系的局部时空自相关指数LSTACFA。在此基础上，采用伦敦LCAP项目中所采集的行车时间数据，用ST-ACF和LSTACFA从全局和局部角度分析伦敦市路网的时空相关性。研究结果表明：伦敦市路网存在着显著的时空正相关，并且这种时空相关性具有时间动态、空间异质等特点，同时揭示了伦敦市时空自相关分布状态以及其变化趋势。通过与CCF指数的对比验证了LSTACFA的合理性和准确性。
Abstract: A method of building adjacency matrix of road network was put forward. A new local spatial-temporal autocorrelation coefficient based on adjacency, LSTACFA was proposed. Based on this, exploratory spatial-temporal autocorrelation analysis was further carried out by using journey time data collected on London’s road network. The results show that through the use of both global and local autocorrelation measures, the autocorrelation structure of London’s road network is found to be dynamic and heterogeneous in both time and space. The distribution of spatial-temporal autocorrelation and its trend are discovered. LSTACFA is accurate by checking with CCF coefficient.
赵玲，王佳璆，邓敏，黄晶
(中南大学 地球科学与信息物理学院，湖南 长沙，410083)
摘 要：以城市道路网络为研究对象，提出一种适用于道路网络的邻接矩阵建立方法，并建立一种基于邻接关系的局部时空自相关指数LSTACFA。在此基础上，采用伦敦LCAP项目中所采集的行车时间数据，用ST-ACF和LSTACFA从全局和局部角度分析伦敦市路网的时空相关性。研究结果表明：伦敦市路网存在着显著的时空正相关，并且这种时空相关性具有时间动态、空间异质等特点，同时揭示了伦敦市时空自相关分布状态以及其变化趋势。通过与CCF指数的对比验证了LSTACFA的合理性和准确性。
关键词：道路网络；交通流；行车时间；时空自相关分析；邻接矩阵
ZHAO Ling, WANG Jia-qiu, DENG Min, HUANG Jing
(School of Geosciences and Info-physics, Central South University, Changsha 410083, China)
Abstract:A method of building adjacency matrix of road network was put forward. A new local spatial-temporal autocorrelation coefficient based on adjacency, LSTACFA was proposed. Based on this, exploratory spatial-temporal autocorrelation analysis was further carried out by using journey time data collected on London’s road network. The results show that through the use of both global and local autocorrelation measures, the autocorrelation structure of London’s road network is found to be dynamic and heterogeneous in both time and space. The distribution of spatial-temporal autocorrelation and its trend are discovered. LSTACFA is accurate by checking with CCF coefficient.
Key words:road network; traffic flow; journey time; spatial-temporal autocorrelation analysis; adjacency matrix
