Real-time road traffic states measurement based on Kernel-KNN matching of regional traffic attractors

The accurate measurement of road traffic states can provide decision making for travelers and traffic managers. In this paper, an algorithm based on Kernel k-nearest neighbors (Kernel-KNN) matching of regional traffic attractors is presented to estimate road traffic states. First, the road traffic running states are divided into several different modes. The concept of the regional traffic attractors of the target link is put forward for effective matching. The representative road traffic state data are extracted to establish the reference sequences of road traffic running characteristics (RSRTRC). Then the sequence of regional traffic attractors is selected and its kernel function is constructed, with which the regional traffic attractors can be mapped into a high dimensional feature space. The reference and current sequences of regional traffic attractors are extracted and the Euclidean distances in the feature space between them are obtained. Finally, the road traffic states are estimated from weighted averages of the selected k road traffic states, which correspond to the k smallest Euclidean distances. Several typical links in Beijing are adopted for case studies. The final results of the experiments are presented, which prove that this road traffic state measurement approach based on Kernel-KNN matching of regional traffic attractors is feasible and can achieve a high accuracy.