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Freeway ramp control study

Author: JiangTao
Tutor: LiangXinRong
School: Wuyi University
Course: Traffic Information Engineering \u0026 Control
Keywords: Highway Ramp control Fuzzy adaptive method Fuzzy RBF Neural Network Genetic Algorithms Iterative Learning
CLC: U491.54
Type: Master's thesis
Year: 2010
Downloads: 100
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Abstract


With the rapid development of the economy and the popularity of the car, the highway traffic congestion has become a serious social problem that troubled governments around the world. In order to effectively solve the problem of highway traffic, on the one hand, you can build more highway; on the other hand can be reasonably regulate and control traffic. The ramp control highway traffic control, the defects of traditional traffic control technology, intelligent control applied to highway traffic control, ramp control several intelligent method discussed in detail, the main work of the thesis is as follows : (1) for the nonlinear and time-varying characteristics of freeway traffic system, designed a fuzzy adaptive PID controller, and applied to freeway ramp metering control. First, the establishment of a transport model to describe the process of highway traffic. Based on this model combined with nonlinear feedback theory, design fuzzy PID ramp controller. The ramp metering rate PID controller decided to adjust PID parameters by fuzzy logic according to the density tracking error and error change. Gaussian curves and trigonometric curves were used to describe the membership function of the fuzzy variables. The fuzzy rule base consists of 49 fuzzy rules. Finally, the control system using MATLAB software simulation results show that the controller has a fast response speed, good dynamic and steady-state characteristics. It enables the main highway running on the desired density, and make vehicles travel more efficiently and safely. (2) ramp controller using fuzzy RBF neural network to deal with freeway traffic flow density problem based on a macroscopic traffic flow model. First, create a macroscopic traffic flow model for the description of highway traffic. Then, the analysis of the structure and function of fuzzy RBF neural network, combined with nonlinear feedback theory design fuzzy RBF neural network tuning PID ramp controller. Based on real-time traffic status, fuzzy RBF neural network dynamically adjust the PID parameters to obtain the minimum density tracking error. Finally, the controller using MATLAB software simulation, simulation results show that the design of the controller has good dynamic and steady-state performance, make the main highway to obtain a desired traffic density. (3) coordinated ramp control based on the traffic conditions in the whole freeway system, it depends only on the local the ramp control compared in the ramp nearby traffic information to improve highway traffic environment has a more promising prospect. A the applied coordination ramp control multilayer control strategy and genetic algorithm optimization. First, the establishment of a macroscopic model to describe the freeway traffic flow changes. 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, the decision to each section of the expectations based on the current traffic status of traffic density; direct control layer to maintain a state variable PI controller actual The value in the desired state near. The genetic algorithm is used to find the optimal PI parameters of the direct control layer. Finally, the simulation results of the control system shows the efficiency and feasibility of the control method. This method can effectively eliminate traffic jams, make vehicles run more efficiently and safely. (4) using the iterative learning ramp metering method to deal with the macro-environment, highway traffic density control problem. First, the establishment of a model used to describe the variation of highway traffic. Then, the traffic density instead of transport share was selected as the control variable. Combined with nonlinear feedback theory, design a control system based on the iterative learning the ramp. Finally, the system simulation using MATLAB software, the simulation results show that the iterative learning method can effectively deal with these problems can be greatly improved traffic speed of response. This method can achieve almost perfect tracking performance, eliminate traffic congestion.

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CLC: > Transportation > Road transport > Technical management of traffic engineering and road transport > Traffic engineering and traffic management > Line of traffic safety facilities > Traffic Control
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