Traffic Prediction Based on SVM Training Sample Divided by Time - Lingli Li; Hongxia Xia; Lin Li; Qingbo Wang (Institute of Advanced Engineering and Science, 2013)
In recent years, the volume of traffic is rapidly increasing. When vehicles running through the tunnel are more intensive or move slowly, the tunnel environment occurs deteriorated sharply, which affects the normal operation of the vehicle in the tunnel. This paper uses the result of previous mining association rules to select feature items and to establish four training samples divided by time. Then the training samples are utilized to create the SVM classification model. Finally the trained SVM model is used to prediction the tunnel traffic situation. Through traffic situation prediction, effective decisions can be made before traffic jams, and ensure that the tunnel traffic is normal. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3656