The Research Of Network Of Green Wave Based On Sub Trunk Model

In recent years, with the social progress and the rapid development of automobile industry, traffic congestion, block and other problems in city road is increasing. Also with the process of urbanization gradually accelerating in China, urban population scale and the car retain increases, which exacerbates the road traffic congestion and results in a series of traffic and environmental problems. Green wave control is one of the main methods of the urban road signal control. The control has the characteristics of obvious effect, simple realization. The design objective is to make as many vehicles through multiple junctions continuously, and reduce the number of vehicle parking and delay time.In order to improve the green wave bandwidth between the intersection and design better green wave bandwidth in practical application, based on the comprehensive green wave control to make further expansion, and proposed the concept of sub trunk model. In practical application, the larger the trunk road traffic load is, the better actual effect of configuration green belt sections, the wider green belt should be given to the route, so first of all, on the route of a system with piecewise, using the mean flow and spacing of sectional trunk as the influencing factors of piecewise weighted green wave, thus based on sub trunk model to establish the optimization model of trunk two-way green wave band. After considering the public cycle on the trunk delay to set cycle constraints that affect the public cycle, giving the test method to calculate the process of sub trunk green wave bandwidth, and the phase difference and common cycle as optimization variables are proposed to make GA optimize sub trunk green wave bandwidth. And based on this, the method of trunk model green wave band control is applied to the traffic network, and adding sequence as the same optimization variables in multiphase, based on sub trunk model to design the network of green wave band. The simulation results show that the method is feasible and effective by comparing with the data in Beijing city.