Prediction of passenger flow on the highway based on the least square support vector machine

A support vector machine is a machine learning method based on the statistical learning theory and structural risk minimization. The support vector machine is a much better method than ever, because it may solve some actual problems in small samples, high dimension, nonlinear and local minima etc. The article utilizes the theory and method of support vector machine (SVM) regression and establishes the regressive model based on the least square support vector machine (LS-SVM). Through predicting passenger flow on Hangzhou highway in 2000-2008, the paper shows that the regressive model of LS-SVM has much higher accuracy and reliability of prediction, and therefore may effiectively predict passenger Flow on the highway.