Research On Methods Of Traffic Flow Forecasting Based On SVM

Accurate real time traffic flow prediction is a prerequisite and key to realize intelligent traffic control and guidance, and it is also the objective requirement to intelligent traffic management. Due to the strong non-linear, stochastic, time-varying characteristics of urban transport system, traditional forecasting methods based on accurate mathematical models is not ideal, therefore, artificial intelligence methods obtain more and more attention. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory (SLT), it can efficiently solve small samples, nonlinear, high dimensions and local minima problems, and it is a research front of complex non-linear science and artificial intelligence scientific research. In this paper, the research situation of traffic flow prediction models are summarized, the advantages and disadvantages of different models is compared. Based on the analysis of the correlation of traffic flow data and important factors which affect the traffic flow's change, the support vector regression method and its application in traffic flow prediction are systematically discussed, such as the support vector machine's parameter selection method, kernel function construction method, incremental learning technique, parallel computing technology and its application in traffic flow prediction, main tasks are as follows:(1) Research on the SVM parameter selection. The selection of insensitive loss coefficient, penalty coefficient C, kernel function and its parameters is important to the learning accuracy and generalization ability of regression model. In this paper, according to the traffic data properties, a traffic flow prediction method with adaptive parameter selection is proposed, using the training set to solve support vector regression parameters. Compared with traditional experience selection SVR method, adaptive parameter selection SVR method can adaptively select parameters based on training set, realize the model's adaptive capacity and efficaciously improve traffic flow prediction accuracy.(2) Research on support vector machine kernel function's construction method. Since the widely used Gaussian (Radial Basis Function) kernel function can not does arbitrary signals approximation well, especially the boundary approximation and multi-scale signals approximation, the wavelet is used which has good multi-resolution time-frequency characteristics and approximation properties, and SVR with Marr wavelet kernel function and multi-scale kernel function are constructed. With simulation test analysis, this method has better generalization capability and training speed, make up the approximation performance deficiencies of traditional kernel functions and solve the stochastic interference factors' enormous influence and strong uncertainty problems in real time traffic flow prediction.(3) Research on support vector machine's parallel computing technology. In the dynamic traffic flow prediction in large-scale network, parallel computing is an important solution to solve the large sample set's fast training problems in short time traffic flow prediction. With the research on parallel SVM, parallel SMO is employed in traffic flow prediction. Simultaneously,2000 sections experiments are carried on the DeepComp 1800 high-performance machine. The experimental results demonstrate parallel SVR is practicable and effective to predict traffic flow in large-scale traffic network.