Research On The Technology Of The Choice And Implementation Of The Regional And Intelligent Traffic Control Strategy

The running process of traffic flow in urban road is extremely complex, it affected by many factors, such as road conditions, traffic flow composition, the drivers'proficiency and so on. In different road conditions and traffic management conditions, the operation characteristic of traffic flow will change a lot, and the corresponding control objectives and control strategies are different. The practice shows that when the advanced signal control strategy is used for the actual signal control systems, the control effect may not be good. Because any kind of control strategies are based on a certain road traffic conditions, under the particular road traffic conditions, the traffic signal control strategy can achieve the best results.The existing traffic signal control systems is used either off-line optimization(TRANSYT), can not adapt to the dynamic changes of the traffic flow, or online optimization mode(SCATS), the optimization degree is low without real-time traffic model, even if there is traffic flow model(SCOOT), can not response to the traffic demand timely, has not yield satisfactory control results. In view of the above problems, this paper introduced the selective model of regional traffic signal control strategy which can response to the real-time traffic conditions. It can complete the conversion of the traffic signal control strategies based on real-time regional traffic conditions.Chapter 1: Introduction. This chapter firstly introduced the research background of this paper, and reviewed the main traffic signal control strategy at home and abroad, based on this, then introduced the significance of this paper, finally, it presented the main content of this paper.Chapter 2: The treatment of the traffic data. Because the error is often produced in the process of collection of traffic data, this paper introduced the main method of pretreatment of traffic data, and presented the fuzzy cluster method of traffic data.Chapter 3: Research on the identification method of taffci condition based on cloud model. In the process of the identification of traffic condition, there is greater fuzziness and randomness, cloud model is a transformable model of qualitative and quantitative, and it integrates the fuzziness and randomness, considering the characteristic of the identification of traffic condition and the advantage of cloud model, this paper introduced the identification method of taffic condition based on cloud model, and it is validated based on simulative data.Chapter 4: Research on the technology of the choice and implementation of the regional and intelligent traffic control strategy based on fuzzy neural network. Firstly this chapter analyed various taffic signal control strategies, and confirmed the adaptive conditions of every traffic signal control strategy. The technology of fuzzy neural network is introduced in briefly, then it introduced the choice model of regional and intelligent traffic control strategy based on fuzzy neural network, and the model is validated based on simulative data of vissim.Chapter 5: Summary and Outlook. This chapter summarized the whole research process, and then put forward to the deducts need to be improved and the further research work.