Research And Application Of Short-term Traffic Flow Forcasting Based On Support Vector Machine Regression

Short-term traffic flow forecasting techniques is an important part of the research of intelligent traffic control and vehicle-induced field. As a very important basic theory of intelligent traffic system, it will be use of urban intelligent traffic induction and help users select path based on traffic flow analysis and forecasting in actual engineering.Support vector machine can solve those problems such as over learning, less learning, local minimum, small samples, which can not be solved very well in traditional machine learning research. So we can use this theory in subject of short-term traffic flow regression forecasting.The subject named research and application of short-term traffic flow forecasting based on support vector machine regression discuss and study methods and theory of short-term traffic flow forecasting on the basis of the analysis of traffic flow data, and then we build a forecasting model based on regression.The main work is as follows:1、Analysis the data of traffic flow,and process error and missing data, reduce noise and to lay the foundation for the establishment of the traffic flow forecasting model;2 、 According to the basic principles of support vector machines, based on support vector machine regression of short-term traffic flow forecasting model, experimental results show that support vector machine regression model is a viable, effective traffic flow forecasting model.3、Support vector machine parameter selection optimization model. The penalty factor, kernel function parameter optimization of support vector selection plays an important role in learning precision and generalization ability of the regression model is good or bad. This article uses the traditional particle swarm optimization algorithm, support vector regression parameters, parameter optimization of the parameters; combined with the genetic algorithm, using an improved particle swarm optimization, simulation, improved particle swarm optimization to achieve the prediction model adaptive capacity, and improve short-term traffic flow prediction accuracy.4、The system of Xi ’an for dynamic prediction of short-term traffic flow was designed and implemented under the ThinkPHP framework by using research conclusion of SVR regression prediction of short-term traffic flow.