Data Fusion for Traffic Incident Detector Using D-S Evidence Theory with Probabilistic SVMs

Abstract

Accurate Incident detection is one of the important components in Intelligent Transportation Systems. It identifies traffic abnormality based on input signals obtained from different type traffic flow sensors. To date, the development of Intelligent Transportation Systems has urged the researchers in incident detection area to explore new techniques with high adaptability to changing site traffic characteristics. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, multisensor based traffic incident detector can be viewed as a problem of evidence fusion. This paper proposes a new technique for traffic incident detection, which combines multiple multi-class probability support vector machines (MPSVM) using D-S evidence theory. We present a preliminary review of evidence theory and explain how the multi-sensor traffic incident detector problem can be framed in the context of this theory, in terms of incidents frame of discernment, mass functions is designed by mapping the outputs of standard support vector machines into a posterior probability using a learned sigmoid function. The experiment results suggest that MPSVM is a better adaptive classifier for incident detection problem with a changing site traffic environment.