Research On Signal Dynamic Control Based On Big Data Mining

As China's urbanization process continues to accelerate,the number of motor vehicles continues to increase,and the speed of road infrastructure construction is relatively slow,which makes some intersections in the urban road network oversaturated during rush hours.As a result,traffic congestion on the main line and the area is caused,which seriously affects the efficiency of public travel.Based on the background of big data development,this paper first analyzes the impact of traffic big data development on signal control,and systematically elaborate data mining,preprocessing and control parameter extraction technologies;Then,a new method of timing optimization is proposed for the coordination of single intersections and arterial under saturation.In terms of timing optimization for single intersections,the release characteristics of vehicles under high-load conditions at intersections are first analyzed,and the space-time characteristics of vehicle traffic under different load conditions are studied.Finally,for the characteristics of low traffic flow release efficiency at the end of the green light,the loss time of the signal cycle and the green light utilization rate are used as optimization indicators to establish an intersection timing optimization model.In terms of dynamic control of arterial coordination: firstly,the characteristics of arterial traffic flow under different saturation conditions are analyzed.In view of the defect that the green wave coordination model is likely to cause secondary parking at intersections under high load conditions,and then a quasi-green wave arterial coordination optimization model for the transition period with a delay as the optimization goal is established.Finally,taking the intersection of Nanjing Road-Xincun West Road and Beijing Road in Zhangdian District,Zibo City as examples,the optimization model proposed in this paper was verified.(1)Nanjing Road-Xincun West Road intersection: After the signal timing optimization of the intersection is implemented,the loss time at the intersection is reduced by 12.7% and the queue length is reduced by 20%;(2)Beijing Road: After implementing the quasi-green wave arterial coordination optimization during the transition period,the average delay of vehicles in the coordinated direction is reduced by 28%,and the average travel speed is increased by 13.2%,which effectively improves the capacity of vehicles during the transition period.