Coordination Control Of Urban Traffic Networks

Control of urban traffic networks is a popular topic in control domain and traffic engineering domain at home and abroad in these decades. With the increase of the number of vehicles and the density of traffic networks, mutual influence of traffic flows among adjacent intersections is gradually strong. To increase efficiency of urban transportation, new urban traffic control technique regards the whole traffic networks as a large scale system and coordinatively controls every intersection at the same time. With the fast development of automatic control, computer technique, communication technique and traffic engineering technique, many traffic flow models and coordination control methods have been found. New theory and research achievements have been published in recent years. Some applications in engineering have shown their tremendous powers.In this dissertation we mainly study and analyze coordination control technique of urban traffic networks. We make use of intelligent hybrid forecasting method to forecast urban short-term traffic flow which is heavily nonlinear, stochastic, time-variant and uncertain. Moreover we describe the structure of intelligent hybrid forecasting method. We also design some advanced intelligent traffic control algorithms by use of neural network, fuzzy logic, genetic algorithm and large scale system theory. Simulation analysis and application results show that these algorithms are more robust, self-adaptive and self-learning, and can solve urban traffic problems more effectively than conventional traffic control methods.The main work and contributions of this dissertation are as follows:1. We make an overview on the generation, development and last achievements of urban traffic control technique at home and abroad in detail, and make a discussion on difficulties and problems in theory analysis and actual application. Combined with specific characteristics of urban traffic in our country, we present the future research direction on urban traffic control technique.2. In order to transcend the limitation of existing single forecasting technique on different traffic condition, a novel intelligent hybrid (IH) method for short-term traffic flow forecasting is presented. The IH method has 3 sub-modules: history mean (HM) module, artificial neural network (ANN) module and fuzzy combination (FC) module. The HM method has good static stabilization character. The ANN method can estimate the dynamic traffic flow in a very precise and satisfactory sense. In order to take advantage of the useful information of the HM module and the ANN module to improve the forecasting effect further, the two individual modules reflecting practical problems from different respects are combined by fuzzy logic. The FC module mixes the two individual forecast results and its output is regarded as the final forecasting of the traffic flow.3. A dynamic two-direction green wave intelligent control strategy is presented. The whole control structure is divided into the coordination layer and the control layer. Public cycle time, up-run offset, down-run offset and splits on the arterial are calculated in the coordination layer, and the splits of each intersection on the arterial are adjusted in the control layer at the end of each cycle. Public cycle time is adjusted by fuzzy logic according to the saturation degree of key intersection on the arterial. The offsets are calculated by average speeds. The variable splits of each intersection are adjusted based on historical and real-time traffic information. The target is to decrease vehicle average delay time and make vehicle stop as little as possible.4. An intelligent coordination control method of urban region traffic is presented. A fuzzy signal controller, including phase choosing module, green observation module and decision module, is installed at each intersection. Fuzzy signal controllers cooperating with each other can optimize phase sequence and phase length. In order to make the system robust, the fuzzy rules are optimized by genetic algorithm.5. On the basis of distributed road traffic control framework, fuzzy theory and artificial neural networks technique, an intelligent coordination control technique of traffic networks with bus priority is proposed. The whole traffic network is regarded as a large scale system and the subsystems are the intersections. Multi-phases intelligent signal controller that controls its own traffic and cooperates with its neighbors is installed at each intersection. The hard core of signal controller is composed by 3 fuzzy modules. In order to improve control system's robusticity, the fuzzy relation of each module is implemented by a neural network respectively. The target of this proposed method is that through exchanging information from its own traffic detectors and its neighbors and cooperating among adjacent signal controller, social vehicle coordination and bus priority in whole traffic network are realized.6. We design intelligent coordination control system of traffic network with bus priority in Shaoxing City. The target is realizing social vehicle coordination and bus priority in traffic network of Shaoxing, increasing traffic capability, decreasing vehicle travel time and delay time. On the basis of analyzing the traffic situation of Shaoxing, we present the concrete design plan, which describes system structure, control plan, software and hardware design in detail. Moreover, we introduce the functions of the related modules. Finally, expected effects and evaluation criterion of intelligent coordination control system of traffic network with bus priority in Shaoxing City is discussed.Finally, we make a conclusion on current work and propose the future research directions.