Freeway Ramp Control

With the rapid development of economy and the prevalence of cars, freeway traffic congestion has become serious social problems that puzzle the governments around the world. In order to effectively solve the problems of freeway traffic, it is necessary to build more roads. On the other hand, it is needed to regulate and control traffic volume properly. On-ramp control is considered as an important component of freeway traffic control. For the drawbacks of traditional traffic control techniques, intelligent control is applied to freeway traffic in this paper. Several intelligent methods of on-ramp control have been studied in detail. The main contribution can be stated as follows:(1) Aiming at the nonlinear and time-varying characteristics of freeway traffic system, a fuzzy self-adaptive PID controller is designed and applied to freeway ramp metering. A traffic flow model to describe the freeway flow process is firstly built. Based on the model and in conjunction with nonlinear feedback theory, a fuzzy-PID ramp controller is then designed. The ramp metering rate is determined by the PID controller whose parameters are tuned by fuzzy logic according to the density tracking error and error variation. Gauss and triangle curves are used for the membership functions of the fuzzy variables. The rule base including 49 fuzzy rules is also established. Finally, the control system is simulated in MATLAB software. The results show that this controller designed has fast response, good dynamic and steady-state performance. It can achieve a desired traffic density along the mainline of a freeway, and can make vehicles travel more efficiently and safely. (2) Fuzzy RBF neural network is applied to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. Firstly, a macroscopic traffic flow model to describe the freeway flow process is built. Then the architecture and function of fuzzy RBF neural network are analyzed. In conjunction with nonlinear feedback theory, a PID ramp controller regulated by fuzzy RBF neural network is designed. According to real-time traffic status, fuzzy RBF neural network is used to adjust the PID parameters dynamically in order to minimize the performance index defined in terms of the density tracking errors. Finally, the controller is simulated in MATLAB software. Simulation results show that the controller designed has good dynamic and steady-state performance, and can achieve a desired traffic density along the mainline of a freeway.(3) The coordinated ramp control depending on the traffic conditions in the whole freeway system rather than the local conditions around independent on-ramps has gained the most respect to ameliorate the freeway traffic situation. A hierarchy control strategy and genetic algorithm optimization for the coordinated ramp control are proposed. The macroscopic model to describe the evolution of freeway traffic flow is firstly built. Then the coordinated ramp control system is designed. There are two control layers in this coordinated control system:the coordination control layer to select traffic models, to adjust the model parameters, and to determine the desired traffic density in each freeway section according to the current traffic state; and the direct control layer to keep the actual values of state variables in the vicinity of the desired state points via PI controllers. Genetic algorithm is used to find the optimal PI parameters of the direct control layer. The detailed simulation for the control system is implemented to illustrate the efficiency and feasibility of the proposed control method. This method can effectively eliminate traffic jams, and make vehicles travel more efficiently and safely.(4) Iterative learning method is applied to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. The macroscopic model to describe the evolution of freeway traffic flow is firstly built. Then traffic density is selected as the control variable in place of traffic occupancy. In conjunction with nonlinear feedback theory, the iterative learning based ramp control system is designed. Finally, the system simulation is carried out using Matlab software. It is shown that the iterative learning method can effectively deal with this class of control problem and greatly improve the traffic response. This method can achieve an almost perfect tracking performance and eliminate the traffic jams.