Abstract
Online traffic state identification plays an important role in achieving the potentials promised by Intelligent Transportation Systems (ITS) on various traffic applications, e.g., real time traffic monitoring systems. Traditional approaches have shown the limitations in either obtaining the necessary pre-determined information or having difficulties in their online implementation. This paper introduces an online agglomerative clustering procedure for freeway traffic state identification using ITS data, represented by three traffic condition variables of flow rate, speed, and occupancy. An optimal fit of the statistical characteristics is provided by maximizing the intra-cluster data point distances and minimizing inter-cluster data point distances through a joint utilization of the Bayesian Information Criterion and the ratio of change of the dispersion measurements. Test results show that the identified freeway traffic states through the proposed procedure are reasonable and consistent with the common understandings on freeway traffic operating conditions.
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