Modeling And Simulation Of Traffic Flow In Complex Road Conditions Based On Group Intelligent Labor Division Theory

With the rapid increase in the number of motor vehicles on the road,the blockage of urban roads has hindered the development of certain areas.Therefore,improving or solving the problem of urban road congestion has important practical significance.Many researchers have joined this research and put forward many theories and solutions.Among these many theories,the early cellular automaton traffic flow model is one of the more famous methods.On the basis of inheriting the original cell model,this paper establishes a traffic flow model suitable for real urban road traffic,and affects the adjustment of traffic flow parameters for the model part,and understands some road traffic phenomena related to traffic flow,so that Use and master these rules to provide a basis for policy formulation by relevant government departments.The work done in this article is as follows:(1)Based on the traditional Nasch model,the Gipps safety distance calculation rule is introduced,and the green intelligence ratio of the intersection is dynamically adjusted in combination with the group intelligence bee labor division to establish a new mixed traffic flow model.Through the adjustment of the relevant parameters in the simulation,the spatio-temporal distribution map of the traffic flow trajectory is drawn,and the influence of different vehicle flow input rate and the random slowing probability of the intersection vehicle on the traffic flow on the road is compared.(2)Based on the ant colony division of labor theory,combined with the mixed traffic flow cellular automaton model,according to the vehicle characteristics and the total vehicle delay,the ant colony division model is extended to the study of multi-section intersection traffic flow.A simulation analysis was performed.Combining the traffic flow characteristics of the intersection,the distance between the traffic lights at the two intersections is compared,and the intersection time optimization model is solved.The green ratio is adjusted by three indicators: capacity,delay time,and number of stops.