Spatial estimation of urban vehicle traffic using kernel methods
Abstract
The authors investigated methods that can be used to estimate traffic volumes on unmeasured road segments. To this end, a new distance metric has been developed based on the traffic volumes measured in different crosssections. Based on the time series of the detectors, measured in the past, their similarity can be quantified. By transforming the pairwise similarities thus obtained, they become suitable for the spatial estimation of vehicle traffic. Using this metric, two kernel-based methods have been presented.
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