Research On Urban Regional Traffic State Identification And The Analysis Approach To Evolution Of Traffic Congestion Based On CTM

The urban traffic system is a distributed and time-varying dynamic complex system with the characteristics of nonlinear and randomness.The quantification and classification for the urban traffic state is prerequisite to realize advanced traffic control and management method in intelligent transportation system.Based on the important project for key project of Zhejiang province "Research and Development of the Vehicle and Road Resources' Intelligent and Dynamic Disposition System "(2009C03016-3),the traffic state identification and the analysis approach to evolution of traffic congestion are researched.A spatial and temporal hierarchical model was developed based on fuzzy C-means clustering for area traffic state analyses to predict area traffic states.The traffic state of the unit of the road network is firstly quantitatively analyzed.Then the cluster centers of each traffic state for the unit of the road network is gained based on the method of fuzzy C-means clustering.After this,the real-time traffic state is recognized and classified combining with real-time traffic data.According to the units' space distribution in the network,the model presents the different kinds of spatial distribution under different traffic states.After the process of traffic state identification,the analysis approach to evolution of traffic congestion based enhanced cell transmission model(CTM)is proposed.Firstly by modeling the cells for target area based CTM,traffic flow is simulated during the cell.Then we can estimate the duration and spatial diffusion of original traffic congestion from time and space scales according to the model.The simulation result based on PARAMICS proves the method is validity.Finally,based on B/S architecture,the software system of analysis for the urban traffic state is designed and developed by J2EE and Oracle.The experimental result indicated that traffic state analysis method proposed in this paper has good performance in the spatial and temporal traffic state identification and making tracks for traffic congestion.And we gained a deeper understanding for the mechanism that controls the behavior of the urban transportation systems.