Spectral Bisection Community Detection Method for Urban Road Networks

Abstract

Coordination control of large-scale urban traffic networks provides a more easy and feasible way to improve mobility in heterogeneous city centers. Urban traffic network partitioning is the foundation and prerequisite for achieving regional control. In order to describe the correlation strength between two adjacent intersections, a quantitative indicator considering the dynamic traffic flows is proposed to depict the congested degree in each link. Based on the simulation data collected from microscopic traffic model, the topology of urban traffic network could be converted into an adjacent matrix. Inspired by the concept of community detection in complex networks theory, spectral bisection method is utilized to find the spatial compactness areas in traffic networks. In addition, the modularity is adopted to evaluate the partitioning results and to point out the next partitioning direction at each step. The simulation is carried out for a peak hour in SUMO. Compared with other classical community detection methods, the proposed approach can obtain the adequate number of subnetworks with less computational complexity.