The categorization of the traffic state into breakdown and non-breakdown is critical to traffic flow analysis and effective traffic management and operations. Due to the data availability, the identification of the traffic states has been mainly based on the three macroscopic measures (speed, occupancy, and volume). Emerging new technologies will allow the collection of microscopic measures that can be used in combination with the macroscopic measures for better recognition of the traffic state. Since stop-and-go operations result in traffic disturbance, this study developed disturbance metrics based on microscopic measures to examine their capability for better traffic state categorization. The utilized disturbance metrics are the number of oscillations (NO) and a measure of disturbance durations in terms of the time exposed time-to-collision (TET). The study found that adding traffic disturbance metrics in the data clustering when identifying the traffic states will result in better traffic breakdown recognition by capturing stop-and-go in the traffic stream.
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