Research On Traffic Flow Dynamics Model Based On Wavelet Neural Network

ITS(Intelligent Transport System)is a comprehensive transportation management system presented in the 21 st century.After monitoring,controlling,and inducing measures on the status of vehicles driving on the road,the traffic flow can be further reasonably distributed in the urban road system,and the effective use efficiency of the urban expressway network can be improved.Traffic guidance and traffic control are the two main components of an intelligent transportation system.The premise of traffic flow induction and control is to predict traffic flow parameters for urban freeway network traffic.In this paper,based on the summary and analysis of the research on the methods of traffic flow prediction models by domestic and foreign scholars,analyzed the advantages and disadvantages of the traffic flow parameter collection method,and used the freeway microwave detector to collect the data.The sensor will be interfered by various uncontrollable factors during the collection process,causing data loss and other problems,the collected data will be cleaned first,and then the subsequent modeling process will be performed after the cleaning is completed.The specific process has the following aspects:(1)The pre-processed data is modeled using 6 classic traffic flow density-speed models,and 3 evaluation indicators are introduced to measure the accuracy of the model.It is found that the classic traffic flow model has limitations and prediction accuracy is low.(2)In order to solve the problem,the data mining method is used to decompose the preprocessed data by EMD,and the coupling degree of the correlation between the data is deeply mined.The trial and error method is used to find a group of reorganized data with the best coupling for the subsequent process.An excellent of reorganized data is modeled by wavelet neural network.In the modeling process,taking into account between the traffic habits during the week and the weekend,this paper will separately predict the traffic flow on weekdays and weekends.At the same time,in order to illustrate the effectiveness of the proposed method,the BP neural network algorithm is used to compare the proposed method.(3)In order to visualize the previous research content,the LabVIEW software was used to program the traffic flow prediction system.6 modules including login module,data preprocessing module,relationship diagram module,EMD decomposition interface module,classic model module,and main interface module are compiled.After the visual module programming,the research results are more intuitive and clearer,which provides a certain reference value for the subsequent construction of smart cities.The model simulation results show that the data modeling after the EMD method is more accurate than the data modeling results without the EMD method.At the same time,the accura-cy of the wavelet neural network model is higher than the BP neural network model.