The complexity of urban traffic road network and the China's car parc are growing at a high speed. Traffic jam now is a common problem in most citys. And 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 of driving through the signal intersections is neglected，which resulting in a large error between the calculation result and the actual driving cost. To solve this problem，a more accurate multi⁃factor traffic network model with road and signal intersection based on Petri nets is built，and a traffic optimal route selection algorithm based on improved elitist ant system is proposed. Improvements on the classical ant colony optimization are provided in two aspects. Firstly，the main road guiding and driving direction guiding are added in the initialization of the pheromone concentration to speed up the initial search; Secondly，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. At the same time，the convergence rate of this algorithm is several times of others’ under the premise that the probability of finding the optimal solution is not less than 90%.

Keywords： traffic ; optimal path ; road network model ; ant colony optimization ; pheromone ; elitist strategy