Research On Traffic State Identification Based On RFID Electronic License Plate

At present,traffic congestion has become increasingly acute in China.It has affected travel efficiency,and even threatened the property safety and life safety of the residents.Besides,resources waste and environmental pollution caused by traffic problems cannot be ignored.Intelligent transportation system is the main way to control the traffic rules and improve the travel efficiency.Intelligent transportation system monitors traffic state by collecting real-time traffic information,analyzing traffic information and mining rules.With the continuous development of RFID technology,RFID technology is more widely used in the traffic field.RFID technology has many advantages in the traffic information collection.For example,RFID technology has a large amount of information storage,and it can be identified quickly.It is also able to adapt to a variety of environments.Because RFID technology can collect the traffic data reliably,it can provide data for traffic state identification.Traffic state identification is the basis that intelligent traffic system realizes road traffic state monitoring and traffic guidance.The traditional traffic state identification uses manual mode.This method is direct and effective,but it needs to invest many human resources.In addition,it is difficult to realize automatic and intelligent control.Therefore,the technology of automatic identification is the focus of the study.The technology of automatic identification has been mature,and it is not limited to the traditional identification technology.Artificial neural network,expert system,mode discrimination and fuzzy theory are applied to the traffic field,and the technology of traffic state identification has improved greatly.This paper uses the theory of traffic flow to extract the main traffic flow parameters based on the data of RFID electronic license plate.The traffic state of the road is identified according to the traffic flow parameters based on the fuzzy theory.The calculation model of congestion intensity is put forward,and the traffic state is quantitatively described.The main work of this thesis is as follows:?The main traffic flow parameters are calculated.The problems of the original data are processed by analyzing the principle of RFID technology.Besides,the traffic flow parameters are extracted based on the theory of traffic flow.And the time characteristics of the traffic flow parameters are analyzed.?Traffic state is identificated.The change of the traffic state is continuous and the limitations of the traffic state are not clear.The classical fuzzy c mean(FCM)algorithm is used to identify the traffic state because of its characteristics and advantages.How the fuzzy weighted index influences the performance of FCM algorithm is studied.The preferable range of fuzzy weighting index is determined.Moreover,the appropriate value is determined by the step size method.?The model of congestion intensity calculation is proposed.The congestion intensity model is established to describe the traffic state quantitatively based on the results of the clustering center obtained by FCM clustering.In addition,the congestion intensity can be used to compare the traffic conditions with different grades of roads.Finally,the result of the experiment shows that the FCM algorithm has better performance in traffic state identification.The result of congestion intensity calculation can reflect the traffic state more intuitively.RFID data can be used as the basic data of intelligent transportation system.