Understanding structure of urban traffic network based on spatial-temporal correlation analysis

Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.

[1]  S. Havlin,et al.  Climate network structure evolves with North Atlantic Oscillation phases , 2011, 1109.3633.

[2]  J. Oeppen.  The identification of regional forecasting models using space-time correlation , 1975 .

[3]  Vito Latora,et al.  The network analysis of urban streets: A dual approach , 2006 .

[4]  A. Nowacki,et al.  Understanding Equivalence and Noninferiority Testing , 2011, Journal of General Internal Medicine.

[5]  Klaus Lehnertz,et al.  Publisher's Note: “From brain to earth and climate systems: Small-world interaction networks or not?” [Chaos 20, 013134 (2010)] , 2010 .

[6]  Jinjun Tang,et al.  Exploring dynamic property of traffic flow time series in multi-states based on complex networks: Phase space reconstruction versus visibility graph , 2016 .

[7]  Leandro Tortosa,et al.  An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector , 2012, Appl. Math. Comput..

[8]  Shuo-Fang Liu,et al.  A scale development of industrial designer ability index through quality function deployment and grey relational analysis methods , 2016 .

[9]  Yinhai Wang,et al.  Dynamic analysis of traffic time series at different temporal scales: A complex networks approach , 2014 .

[10]  Leandro Tortosa,et al.  New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks , 2016, Appl. Math. Comput..

[11]  Yang Yue,et al.  Spatiotemporal Traffic-Flow Dependency and Short-Term Traffic Forecasting , 2008 .

[12]  E. Ben-Jacob,et al.  Challenges in network science: Applications to infrastructures, climate, social systems and economics , 2012 .

[13]  Nitesh V. Chawla,et al.  Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science , 2011, Stat. Anal. Data Min..

[14]  C. F. Bond,et al.  Seeing the FisherZ-transformation , 2004 .

[15]  SATYAM MUKHERJEE,et al.  Statistical analysis of the road network of India , 2012, 1203.0313.

[16]  Michael Batty,et al.  Exploring the evolution of London's street network in the information space: a dual approach. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.