The principle of predictive control is applied to research on real-time dynamic route optimization of traffic travel, and a real-time dynamic route optimization model based on predictive control is proposed. Taking the driving time as the controlled variable, the driving speed as the manipulated variable, some traffic conditions as disturbance factors, the shortest driving time as the set value, the static shortest route as the reference route of the control system, and the objective function with the shortest driving time, which is defined as the control performance is established. According to the change in the road network state and the optimal solution result of the objective function, the real-time dynamic route selection based on the shortest driving time is realized by switching among different static shortest routes, and the rolling optimization and combination of dynamic and static routes are implemented in the process. A unique method is also used to obtain the optimal solution of the objective function in this study, which is scientific, reasonable, fast, and convenient. The optimization model overcomes the shortcomings of determining the dynamic shortest route by depending on traffic flow prediction and speed prediction. The simulation results and case study prove that the predictive control model algorithm of real-time dynamic route optimization is correct and better. The most important feature of the model algorithm is that it takes the static driving route and desired driving time as the control goals, and it can achieve the global optimal solution of the shortest path and shortest time. The proposed model algorithm has good innovation and practical applications.

An expressway is divided into different road segments according to the entrance-exit toll stations. Based on the driving characteristics of expressways, two methods are proposed to predict the average speed of vehicles on an expressway. One is based on the relationship between the number and the average speed of vehicles on road segments, and the other is based on traffic situation obtained by Baidu, Gaode, and other online maps API. According to the methods, intelligent driving strategies are adopted to satisfy the desired driving time and achieve the driving task successfully. The main principle of the strategies is to adjust the driving route and speed automatically according to the expressway conditions and desired driving time, and to realize the switch of the driving route between the expressway and provincial highway, or national highway and other non-expressway networks. The speed prediction methods and intelligent driving strategies overcome the shortcomings of the existing expressway traffic volume prediction. It has no complex model but is simple, feasible, fast, and practical, which provides an important theoretical basis for the design of expressway intelligent driving systems. The proposed methods exhibits good innovation and practical applications.INDEX TERMS Expressway, intelligent traffic, road network big data, traffic volume forecast, route optimization.