Research On Key Problems Of Expressway Network Traffic State

The rapid development of the transportation and the substantial increase in the demand for residents' travels have led to an increasing pressure on road traffic and transportation,especially on the inter-city highway and highway around the big cities.Once the traffic incident occurs,it will enormously affect the road patency even lead to a chain effect,which will have a huge impact on the highway transport capacity.Therefore,in order to realize the effective supervision of the status of highway,it is necessary to intensively study the key problems of highway network traffic state.Thus,based on the floating car trajectory data collected on the highway,this paper progressively studies the highway road information on three aspects: the floating vehicle data denoising,highway road traffic incident detection and highway traffic conditions quantification.The specific research contents are as follows:The temporal and spatial distribution of floating vehicle data determines the feasibility and scope of the research on the status of highway network.Although the massive trajectory data is collected,the strong randomness of the vehicle trajectory leads to uneven distribution of data in time and space,so it is difficult to define the scope of the study.In order to solve this problem,the range of road research and the cycle of road information release are determined by studying the lower limit of data required for road traffic information and the analysis of spatial and temporal distribution of data.In order to solve the problem of noise caused by various types of erroneous data and error data in the collection process of floating vehicle data,a data denoising algorithm based on wavelet threshold is proposed.Constructing the wavelet reconstruction factor,referring to the parameters such as noise variance and signal-to-noise ratio,wavelet basis function filtering is used 6to determine the wavelet basis function suitable for de-noising of floating vehicle data.Then a threshold function was constructed to overcome the shortcomings of soft and hard threshold function.Finally,the number of decomposed layers is determined on the measured data to realize the wavelet threshold denoising algorithm for the floating vehicle data.The experiment proved that the algorithm is superior to other algorithms in the data set of this paper,which can better complete the de-noising.Considering that the current traffic incident detection is based on fixed monitor data,which makes the test result of the road coverage is low and the algorithm is more complex.Based on the floating car data of de-noising,this paper analyzes the traffic flow characteristics of traffic incident in highway.Firstly,the normal data elimination algorithm is used to filter the traffic incident data to reduce the subsequent calculation.Then,based on the advantages of wavelet theory in singularity detection,the wavelet traffic incident detection algorithm based on smooth wavelet function is constructed to realize high precision and fine granular traffic incident detection on highway.The experimental results show that the proposed method is superior to other algorithms,and can better complete the road traffic incident detection.Based on the results of road traffic incident detection and through the analysis and extraction of the related factors related to the running state of the highway network,a new Radial Basis Function Neural Network-based bagging algorithm for quantization of highway state is proposed.Bagging integrates several RBF neural networks with different number of neurons,and achieves the relationship between the factors of road running state and its complicated mapping.The comparison of proposed algorithm's test result and the highway traffic calibration data shows that the algorithm can quantify accurately,and the quantization effect on the data is superior to other algorithms.