Traffic Congestion Identification and Analysis of Urban Road Network Based on Granular Computing

Abstract

This paper proposes a generalized model based on the granular computing to recognize and analyze the traffic congestion of urban road network. Using the method of quotient space to reduce the attributes associating with traffic congestion, the identification of traffic congestion evaluation system is established including 3 first class indexes and second class indexes of 11. The weight of evaluation indexes are sorted by value in descending order, which are calculated based on rough set theory. In order to improve the efficiency of traffic congestion identification, the appropriate granular is determined by the model parameter μ. When μ is larger, the identification is more effective and the run time of model is longer conversely. Experiments show when the value of μ is between 0.8 and 0.98, the effect of traffic congestion identification is comprehensive optimal.