ISSN : 1796-203X
Volume : 3    Issue : 10    Date : October 2008

Data Fusion for Traffic Incident Detector Using D-S Evidence Theory with Probabilistic SVMs
Dehuai Zeng, Jianmin Xu, and Gang Xu
Page(s): 36-43
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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.

Index Terms
traffic incident detector, evidence theory, support vector machine, data fusion, pattern recognition