  Traffic accidents have been a severe issue in metropolises with the
development of traffic flow. This paper explores the theory and application of
a recently developed machine learning technique, namely Import Vector Machines
(IVMs), in real-time crash risk analysis, which is a hot topic to reduce
traffic accidents. Historical crash data and corresponding traffic data from
Shanghai Urban Expressway System were employed and matched. Traffic conditions
are labelled as dangerous (i.e. probably leading to a crash) and safe (i.e. a
normal traffic condition) based on 5-minute measurements of average speed,
volume and occupancy. The IVM algorithm is trained to build the classifier and
its performance is compared to the popular and successfully applied technique
of Support Vector Machines (SVMs). The main findings indicate that IVMs could
successfully be employed in real-time identification of dangerous pro-active
traffic conditions. Furthermore, similar to the "support points" of the SVM,
the IVM model uses only a fraction of the training data to index kernel basis
functions, typically a much smaller fraction than the SVM, and its
classification rates are similar to those of SVMs. This gives the IVM a
computational advantage over the SVM, especially when the size of the training
data set is large.
