Journal of Geo-information Science >
Spatial Autocorrelation of Urban Road Traffic Based on Road Network Characterization
Received date: 2012-11-29
Revised date: 2012-12-05
Online published: 2012-12-25
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.
DUAN Ying-Ying, LIU Feng . Spatial Autocorrelation of Urban Road Traffic Based on Road Network Characterization[J]. Journal of Geo-information Science, 2012 , 14(6) : 768 -774 . DOI: 10.3724/SP.J.1047.2012.00768
[1] 姜桂艳,郭海锋,孟志强,等. 基于实时信息的城市道路交通状态评价指标体系研究[J].交通与计算机,2007,25(5):21-24.
[2] 熊志华,姚智胜,邵春福. 基于路段相关的路网行程时间可靠性[J].中国安全科学学报,2004,14(10):81-84.
[3] Jiang B, Yin J, Zhao S. Characterizing the human mobility pattern in a large street network[J]. Physical Review E, 2009, 80(2): 21136.
[4] Han X P, Hao Q, Wang B H, et al. Origin of the scaling law in human mobility: hierarchy of traffic systems[J]. Physical Review E, 2011, 83(3): 36117.
[5] Elhorst J P. Specification and estimation of spatial panel data models[J]. International Regional Science Review, 2003, 26(3): 244.
[6] Cheng T, Haworth J, Wang J. Spatio-temporal autocorrelation of road network data[J]. Journal of Geographical Systems, 2011,1-25.
[7] Min X, Hu J, Zhang Z. Urban traffic network modeling and short-term traffic flow forecasting based on gstarima model [C].// Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, 2010, 1535-1540.
[8] Ding Q Y, Wang X F, Zhang X Y, et al. Forecasting traffic volume with space-time ARIMA model[J]. Advanced Materials Research, 2011, 156: 979-983.
[9] Porta S, Crucitti P, Latora V. The network analysis of urban streets: A dual approach[J]. Physica A: Statistical Mechanics and its Applications, 2006, 369(2): 853-866.
[10] 李振龙.城市交通分区的探讨[J].交通运输系统工程与信息,2005,5(6):82-86.
[11] 李润梅,宫晓燕. 面向动态交通分配的城市道路网络分区研究[J].中国科学院研究生院学报,2006,23(4):520-526.
[12] 梅娟,何胜,王正祥,等.基于网络模块性的蛋白质序列聚类[J].食品与生物技术学报,2010,29(1).
[13] 杨楠,弓丹志,李忺,等. Web社区发现技术综述[J].计算机研究与发展,2005,42(3):439-447.
[14] Girvan M, Newman M E J. Community structure in social and biological networks [C]. Proceedings of the National Academy of Sciences, 2002, 99(12): 7821.
[15] Raghavan U N, Albert R, Kumara S. Near linear time algorithm to detect community structures in large-scale networks[J]. Physical Review E, 2007, 76(3): 36106.
[16] Fortunato S. Community detection in graphs[J]. Physics Reports, 2010, 486(3-5): 75-174.
[17] Newman M E J. Networks: An introduction[M]. Oxford University Press, 2010.
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