Research On Traffic Flow Analysis And Traffic Signal Control Strategies At Intersection

With the increasingly serious congestion problems at the urban intersection,people have paid more and more attention on the optimization of the traffic efficiency in urban.In this paper,traffic efficiency of intersection is optimized to ensure that vehicles can drive smoothly and the hoarding vehicles result from traffic emergency can dissipate quickly.The innovations and contributions are summarized as follows:A car following model is improved.Feedback control,adaptive control and lane changing strategy are used to optimize the car-following model.Constancy of a following car’s velocity is ensured via the feedback control signal,which is the speed difference between a leading car and a following car.Model reference adaptive control strategy is used to solve the problem on unmeasurable parameters.Traffic jam caused by the leading car can be solved by changing lane of the following car.Lane changing is achieved by quantum bite gate,which compares cars’ speed and location in two adjacent roads.Based on the simulation,several comparison results verify the effectiveness and feasibility of the improved model.A methodology is improved for real-time detection of traffic flow at a single intersection according to three indicators which are average speed of vehicle,average acceleration of vehicle and average occupancy of road.Three city road AID algorithms based on perceptron,BP(Back Propagation)neural network and BP(Back Propagation)neural network optimized by genetic algorithm are put forward tentatively.A city road AID algorithm based on BP(Back Propagation)neural network is proposed to solve the problem that classification algorithm of perceptron is useful only when the samples are linearly separable.Besides,genetic algorithm is selected to optimize the weights and thresholds of BP neural network.In the simulation,several comparison results verify the effectiveness and feasibility of the proposed algorithms.The arrival delay time model is improved and the improved arrival delay time model is given which is combined with quantum information technology at any time.The optimization function is designed based on vehicle delay,vehicle parking times and traffic capacity.Traffic signal timing scheme is extended for traffic emergencies by using improved climbing method at the urban road intersection.The simulation,The simulation proves that signal timing scheme is effective at transport emergency.In this paper,a car following model is improved.Feedback control,adaptive control and lane changing strategy are used to optimize the car-following model.City road AID algorithms based on neural network are designed.According to the traffic flow parameters determine whether traffic congestion occurs.According to the comprehensive optimization of objective function,the intersection signal timings again to solve the problem of the increasing delay time,the increasing number of vehicle parking and the weakening traffic capacity caused by traffic congestion at the intersection.