K-MEANS CLUSTERING METHOD TO CLASSIFY FREEWAY TRAFFIC FLOW PATTERNS

In this paper, performances of multivariate clustering methods in specifying flow pattern variations reconstructed by a macroscopic flow model are sought. In order to remove the noise in and the wide scatter of traffic data, raw flow measures are filtered prior to modeling process. Traffic flow is simulated by the cell transmission model adopting a two phase fundamental diagram. Flow dynamics specific to the selected freeway test stretch are used to determine prevailing traffic conditions. The classification of flow states over the fundamental diagram are sought utilizing the methods of partitional cluster analyses by considering the stretch density. The fundamental diagram of speed-density is plotted to specify the current corresponding flow state. Non-hierarchical or partitional clustering analysis returned promising results on state classification which in turn helps to capture sudden changes on test stretch flow states. The procedure followed by multivariate clustering methods is systematically dynamic that enables the partitions over the fundamental diagram match approximately with the flow patterns derived by the static partitioning method. The measure of determination coefficient calculated by using the K-means method is comparatively evaluated to statistically derive this conclusion.