Application of Ant Colony Algorithm in the Field of Urban Intelligent Transportation

Transportation strongly promotes the development of the national economy and plays a vital role in modern society. But at the same time, the development of transportation has also brought great problems. Traffic accidents have caused a large number of deaths and economic losses, and road traffic has caused uncoordinated surrounding environment, deteriorating environment, and rapid energy consumption. Intelligent transportation system (ITS) can provide an effective way to solve the above problems, effectively coordinate traffic management, and provide users with more comfortable and effective travel or transportation modes. Besides, it can also make full use of energy by means of increasing the flow of the road network, reduce the exhaust emission of vehicles, and solve the increasingly serious environmental problems. It can also create a better environment for transporting goods and promote the development of the national economy. Therefore, this paper has completed the following tasks: 1) Briefly introduced the background of this paper and the significance of the optimal path planning algorithm research, expounded the existing path planning status, and affirmed the significance of the path planning algorithm. 2) The ant colony algorithm (ACA) is described in detail, including the basic model, algorithm steps, and algorithm characteristics of the ACA, and the parameters of the ACA are discussed. Then the improved ACA and hierarchical ACA are proposed in this paper. 3) The data map used in the experiment is designed, and then the simulation software is used to carry out the experiment. The results show that the improved ACA proposed in this paper has superior performance.