Journal of Geo-information Science >
Spatial Autocorrelation Analysis of Urban Road Traffic Based on Topological and Geometric Properties
Received date: 2014-03-03
Revised date: 2014-04-15
Online published: 2014-05-10
Urban road traffic is spatially autocorrelated. The change of the traffic on a certain road will alter the surrounding roads' traffic status. Understanding the spatial autocorrelation of road traffic is essential for traffic planning and traffic prediction. However, unban road traffic is heterogeneous in spatial, which means that the traffic interactions between neighboring roads are not always isotropy. The spatial heterogeneity of urban traffic makes the measurement of spatial autocorrelation more complex, thus only uses spatial adjacency to define the traffic autocorrelated roads cannot well reveal the characteristics of spatial autocorrelation in urban road traffic. It is worth mentioning that urban roads have topological and geometric properties, which are neglected in the previous research. The aim of our research is to analyze the spatial autocorrelation of urban road traffic based on the topological and geometric properties of urban roads. We first investigated the spatial clustering characteristics of urban roads using community detection algorithm, and then depicted the spatial heterogeneity of the traffic interaction by measuring the importance of road segments with the use of the roads' generalized geometric forms. Based on those analyses, we proposed a novel approach to cluster together the roads whose traffic is spatially autocorrelated. Experiment results for the road network of Beijing indicate that the proposed approach performs better than the approaches that only consider the spatial adjacency or topological structure, which further implies that our approach can capture the spatial autocorrelation characteristics of urban road traffic more reasonably.
LIU Kang, DUAN Yingying, LU Feng . Spatial Autocorrelation Analysis of Urban Road Traffic Based on Topological and Geometric Properties[J]. Journal of Geo-information Science, 2014 , 16(3) : 390 -395 . DOI: 10.3724/SP.J.1047.2014.00390
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