基于道路结构特征识别的城市交通状态空间自相关分析

doi:10.3724/SP.J.1047.2012.00768

地球信息科学学报 ›› 2012, Vol. 14 ›› Issue (6): 768-774.doi: 10.3724/SP.J.1047.2012.00768

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基于道路结构特征识别的城市交通状态空间自相关分析

段滢滢, 陆锋

中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京 100101

收稿日期:2012-11-29 修回日期:2012-12-05 出版日期:2012-12-25 发布日期:2012-12-25
作者简介:段滢滢(1984-),女,湖北襄阳人,博士,研究方向:LBS技术、交通GIS理论与技术、复杂网络分析等。E-mail:duanyy@lreis.ac.cn
基金资助:
国家自然科学基金项目(41271408);国家"863"计划项目(2012AA12A211, 2013AA120305)。

Spatial Autocorrelation of Urban Road Traffic Based on Road Network Characterization

DUAN Yingying, LU Feng

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received:2012-11-29 Revised:2012-12-05 Online:2012-12-25 Published:2012-12-25

摘要/Abstract

摘要：

城市道路交通状态具有空间自相关特征。路段交通状态的变化会很快影响到邻近路段,导致一定空间范围内路段的交通状态发生改变。揭示城市交通状态的空间自相关特征,对交通规划、交通控制与诱导具有重要意义。然而,受到城市路网空间结构和道路拓扑特征的影响,城市道路交通状态的空间自相关并非各向同性,也并非均匀地向上下游扩散,而是有选择性地集中在部分邻近路段上。因此,仅考虑路段地理空间下的上下游邻近性,难以全面度量路段间交通状态的相互影响,识别出交通状态空间相关性强的道路集合。本文借鉴复杂网络分析方法,定量化分析了城市路网的模块化与层次性特征,利用城市路段在空间上的聚集特征和路段在网络中拓扑角色的差异,提出了一种新的交通状态自相关路段邻近性判别规则,即空间邻近且拓扑等价规则,以此规则实现交通状态空间相关路段聚类过程,更好地揭示城市路段之间的交通状态空间相关性。

关键词: 拓扑特征, 空间自相关, 社区识别, 路网结构

Abstract:

Urban road traffic is spatially autocorrelated. The change of traffic on certain road will quickly affect the traffic on nearby roads, which will alter the overall traffic status within a neighborhood. Revealing the spatial autocorrelation structure in urban road traffic is important for traffic planning, traffic controlling and traffic guidance. The traffic interaction between neighboring roads is not isotropy. The traffic change on certain road does not equally spread to each spatially adjacent road, but concentrate on some of them. Thus only using spatial adjacency to define adjacent roads cannot well reveal the spatial autocorrelation in urban road traffic. Recent research has proved that the dynamic flow on networks highly depend on the structure of networks. Characterizing the structure of urban road network is essential to reveal the spatial autocorrelation in urban road traffic. The aim of this research is to reveal the spatial autocorrelation of urban road traffic based on road network characterization. We first investigate the modular character and hierarchal feature of urban road network quantitatively. The modulars in road network are defined as a group of closely connected neighboring road segments and identified by community detection algorithm from complex network theory. The hierarchal feature of urban road network helps to determine the structural importance of road segments. Topological roles are defined based on the structural importance of road segment. Then we provide a novel approach to define adjacent road segments based on the topological roles in spatially adjacent road segments. Two road segments defined as adjacent road segments not only locate in a nearby neighborhood but also have the same topological roles. A set of adjacent roads constitute a spatial related set. Experiment results on the road network of Beijing imply that the spatial related sets identified by the proposed approach can capture the spatial autocorrelation structure of urban road traffic.

Key words: spatial autocorrelation, community detection, road network structure, topological characteristic

段滢滢, 陆锋. 基于道路结构特征识别的城市交通状态空间自相关分析[J]. 地球信息科学学报, 2012, 14(6): 768-774.DOI:10.3724/SP.J.1047.2012.00768

DUAN Ying-Ying, LIU Feng. Spatial Autocorrelation of Urban Road Traffic Based on Road Network Characterization[J]. , 2012, 14(6): 768-774.DOI:10.3724/SP.J.1047.2012.00768

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基于道路结构特征识别的城市交通状态空间自相关分析

Spatial Autocorrelation of Urban Road Traffic Based on Road Network Characterization

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