Research On Traffic Optimal Path Based On Elitist Ant System

With the advancement of urbanization and the increasing living standards of residents,the complexity of urban traffic road network and the global car ownership are growing at a high speed.The quality of people's driving trips is being seriously affected.How to plan a real-time and optimal driving route in a complex and variable traffic network has become a real concern for people when they travel.In most of the current research on the optimal route selection of traffic,only the static traffic road network scenario is considered,and the cost when passing through the intersection is neglected,which resulting in a large error between the calculation result and the actual driving cost.Aiming at this problem,a more accurate multi-factor traffic network model with road and intersection based on the Petri net is established.Then a traffic optimal path selection algorithm based on elite ant colony system optimized by two aspects is proposed.The main research contents are as follows:Firstly,the shortcomings of the traffic network model used in the current study of traffic path problems is introduced.Then,a multi-factor traffic network model with road and intersection based on the Petri net is established,which contains two parts:the road model and signal intersection model.So that the engineering feasibility and accuracy of results when studying traffic path problems can be guaranteed.Based on the model,an urban traffic network with 30 intersection nodes for simulation analysis of traffic optimal path selection algorithm is built in the PTV-VISSIM software.Based on the established unban traffic network,a fast solution to the optimal traffic path selection problem is implemented using elitist ant system.Then,two aspects of the algorithm optimization are proposed.First,the main road guiding and driving direction guiding are added in the initialization of the pheromone concentration to speed up the initial convergence.Second,a dual elitist ant strategy is utilized that the pheromone concentration on the two optimal paths is globally updated in mutually constraints way.This strategy can accelerate the convergence of the algorithm while avoiding the local optimal solution and two optimal paths can be searched to for selection.The simulation results show that the probability of finding the optimal path is increased to 100%while the convergence rate is guaranteed with the proposed algorithm.The simulation results show that the proposed model is more accurate than the traditional static traffic network model.Moreover,the algorithm of this paper can accelerate the convergence speed and greatly improves the accuracy of search results.It is an effective algorithm for solving the traffic optimal path selection problem.