A fast network partition method for large-scale urban traffic networks

In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.

[1]  Edmond Chin-Ping Chang,et al.  How to decide the interconnection of isolated traffic signals , 1985, WSC '85.

[2]  Shu Lin,et al.  Study on fast model predictive controllers for large urban traffic networks , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[3]  R J Walinchus,et al.  REAL-TIME NETWORK DECOMPOSITION AND SUBNETWORK INTERFACING , 1971 .

[4]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Andrew McCallum,et al.  Piecewise pseudolikelihood for efficient training of conditional random fields , 2007, ICML '07.

[6]  Shengrui Wang,et al.  A direct approach to graph clustering , 2004, Neural Networks and Computational Intelligence.

[7]  Abraham Kandel,et al.  Graph Representations for Web Document Clustering , 2003, IbPRIA.

[8]  Hu Jian-ming Dynamic subdivision of road network into coordinated control regions , 2009 .

[9]  M. Fiedler Algebraic connectivity of graphs , 1973 .

[10]  Alex Pothen,et al.  PARTITIONING SPARSE MATRICES WITH EIGENVECTORS OF GRAPHS* , 1990 .

[11]  David D. Jensen,et al.  Graph clustering with network structure indices , 2007, ICML '07.

[12]  Enhong Chen,et al.  Finding Community Structure Based on Subgraph Similarity , 2009, CompleNet.

[13]  Dewei Qi,et al.  Photocatalytic activity of nano-polycrystalline titania , 2009 .

[14]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[15]  Markos Papageorgiou,et al.  Efficiency and equity properties of freeway network-wide ramp metering with AMOC , 2004 .

[16]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[17]  H N Yagoda,et al.  SUBDIVISION OF SIGNAL SYSTEMS INTO CONTROL AREAS , 1973 .

[18]  Amedeo Caflisch,et al.  Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Massimo Marchiori,et al.  Method to find community structures based on information centrality. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Yi-Chang Chiu,et al.  Urban traffic signal control network automatic partitioning using laplacian eigenvectors , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[22]  T. S. Evans,et al.  Complex networks , 2004 .

[23]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Angela Di Febbraro,et al.  Urban traffic control structure based on hybrid Petri nets , 2004, IEEE Transactions on Intelligent Transportation Systems.

[25]  Ulrik Brandes,et al.  On Modularity - NP-Completeness and Beyond , 2006 .

[26]  Chen Hua-jie Study on traffic zone division based on spatial clustering analysis , 2009 .

[27]  Sophie Midenet,et al.  The real-time urban traffic control system CRONOS: Algorithm and experiments , 2006 .

[28]  Amedeo Caflisch,et al.  Multistep greedy algorithm identifies community structure in real-world and computer-generated networks , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Kai Lu,et al.  Research on Fast Dynamic Division Method of Coordinated Control Subarea: Research on Fast Dynamic Division Method of Coordinated Control Subarea , 2012 .

[30]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[31]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  G. Caldarelli,et al.  Detecting communities in large networks , 2004, cond-mat/0402499.

[33]  Bart De Schutter,et al.  Fast Model Predictive Control for Urban Road Networks via MILP , 2011, IEEE Transactions on Intelligent Transportation Systems.

[34]  Brian W. Kernighan,et al.  An efficient heuristic procedure for partitioning graphs , 1970, Bell Syst. Tech. J..

[35]  Gao Zi-You Research on Problems Related to Complex Networks and Urban Traffic Systems , 2006 .