Coordinated Control And System Design Of Green Wave Signals For Dynamic Arterial Roads Based On Traffic Flow Prediction

The development of urban road traffic is accompanied by many problems,such as high accident rates,traffic congestion,carbon emissions,and air pollution.With the gradual promotion of modern computer technology,intelligent transportation systems have gradually become more widespread in urban road management and highway traffic applications.By providing traffic management departments with a large amount of traffic data,analyzing urban road traffic conditions,using big data mining and artificial intelligence technology,visually presenting traffic processing solutions for decision makers and providing a reliable basis for solving traffic congestion.Based on the traffic data collected by forward-tracking microwave radar vehicle detectors,this paper performs relevant data mining and processing to analyze the current urban traffic conditions.The main tasks completed are as follows: First,for short-term traffic prediction problems A new seasonal least squares support vector regression model is proposed.In the new SD-LSSVR model,a seasonal difference strategy is first used to weaken the seasonal characteristics of the original data series,and then an LSSVR model is constructed to quickly obtain prediction results.In addition,the quantum particle swarm optimization algorithm is used to select the optimal parameters involved in the new model to obtain the best accuracy.The second is to develop a control method for optimizing and coordinating urban road network traffic signals based on data obtained from traffic flow prediction.This method optimizes the phase difference and combines the calculated traffic flow at each intersection to design a coordinated control scheme for trunk green wave signals to reduce traffic congestion and road failures caused by signal timing loss.The experimental numerical results show that the newly proposed prediction model can not only effectively predict the seasonal changes of traffic,but also has a great performance compared with several wellknown prediction models such as BP neural network,seasonal gray prediction,and seasonal ARIMA.Degree of improvement.It adopts a method corresponding to the flow data and signal coordinated control scheme,and responds to the current main road signal timing in real time according to different traffic conditions.The dynamic trunk road green wave signal coordinated control system based on traffic flow prediction realized in this paper can intuitively show the capacity of road intersections and provide effective suggestions for traffic management departments.