Research On Intelligent Control Method And Its Application For Junction Road Network Consist Of Freeway And Expressway

Traffic congestion in metropolitan periphery road networks are growing seriously. However, these areas are lack of control due to the separation of the management system. In the junction road network, the expressway and urban roads are highly loaded and concentrated. Furthermore, the traffic volumes with different properties interchange and conversion with each other, which easily leads to traffic congestion that will quickly spread. The congestion in these parts could reduce the overall capacity of the road network, which has been obviously limited the efficiency of the city's operations. Therefore, the issue of the road junction has become an important factor which restricts the development of city and needs to be solved urgently. Meanwhile, it is a complex systematic problem faced in the traffic planning and management. This paper utilizes the theory of system engineering and combines it with artificial intelligence, traffic engineering, mathematical statistics. According to the structural features of the traffic control on the junction road network, it attempts to solve this complex systematic problem with the intelligent cooperative control technology and integrated control technology based on the emulation technique.The research findings of this paper include:(1) The different standards, managements and isomerism of the traffic data of expressway and urban road have been full considered, on the basis of which the blending mode of isomerous data was discussed. After that, this paper analyzed the changing regularity of relevance among traffic volume, sections and ramps with grey correlation analysis. It also made the first-order difference modification and second-order difference modification on trend relational degree and built the second-order trend relational model of the junction road network.(2) Associating with the uncertainty, dynamism, relevancy and complementarities of the traffic volumes that enter and exit the city from the expressway and according to the traffic data of sections, the short-term traffic volume prediction model was built sections traffic data based on Radial Basis Function Neural Network. Then, on the basis of the assumption that the traffic volume is continuous, the traffic pressure formula was built, so was the decision model of multi-section dynamic service levels of junction road network based on the results of the short-term traffic volume prediction.(3) This paper has proposed the hierarchical intelligent control structure of junction road network according to the optimization objectives, which are preventing the congestion, postponing the invalidation of traffic control and improving the traffic capacity of the junction road network. Then this paper built the self-adapted and full-systematic intelligent cooperative control model based on hierarchical intelligent control model with the applying of SWARM.(4) In this paper, road conditions and climate are taken as constraint conditions of traffic safety and efficiency and the speed of vehicle as control variable. Starting with the traffic volume condition function, this paper built the traffic speed control model and designed the main lanes speed controller in junction road network.(5) Considering the influence of main lanes and ramps on traffic control, this paper applied the theory and model creation method of intelligent cooperative control to build relevant main lanes multi-ramp integrated control model of junction road network. Finally, it used empirical analysis to prove that considering main lanes and ramps integrated control on the junction road network could resolve or ease the traffic congestion. It could also maintain the stability of the main lanes and improve the overall capacity of the junction road network.