Car-Following Behavior Model Based on Least Squares Support Vector Machine
QIU Xiaoping
LI Na
Abstract：The least squares support vector machine ( LS-SVM) algorithm describes and fits the car following behaviors on roads with Chinese traffic flow characteristics .The study uses the LS-SVM model to simulate the car-following behaviors on single-lane road.It uses NGSIM data to verify LS-SVM model and also tests the model against traditional Gipps model .The results show that the accuracy of error indicators of the LS-SVM model is improved more significantly than the Gipps model .The LS-SVM model is able to display the potential relationships between variables and compensate the shortage of traditional car-following model .
Keywords：car-followingmachine learningleast squares support vector machineregression forecast
Publication Date：2016-01-01
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：6( 52-57 )