Intelligent traffic cloud computing system based on ant colony algorithm

With the growth of data volume in transportation system, requirements of big data technologies are rapidly increasing. This paper presented an improved ant colony algorithm by using data analysis technologies of cloud computing and data mining. And the influence of different spatio-temporal feature fusion methods on the steering wheel angle value of intelligent vehicles is explored by feature fusion method. Furthermore, time-constrained and space-constrained networks are utilized to extract the key features that affect the steering wheel angle value. Experimental results show that the proposed algorithm improves the efficiency of data processing and information search by 35%, comparing to traditional ant colony algorithm. It is very effective in the shortest path analysis of ITS. Our research shows that the application of real-time information in the logistics distribution system can make the planning process more dynamic and the prediction results closer to reality.