Traffic Incident Duration Prediction Based on Support Vector Regression

Prediction of incident duration is very important in Advanced Traffic Incident Management (ATIM). Predicting accurately the duration of a traffic incident is necessary to effectively reroute traffic around the incident and to clear traffic, in general, away from the incident's area. In this paper, Support Vector Regression (SVR) is employed to predict the incident duration. In our experiments, we used one incident data set collected from an expressway in the Netherlands. The data set is divided into two parts; one is for model development, and the other is for the model validation. The experiment results, including error analysis, indicate that the prediction model based on Support Vector Regression (SVR) can obtain high accuracy for incident duration. This model can effectively be applied to traffic incident detection and clearance systems.