  The utilization of traffic conflict indicators is crucial for assessing
traffic safety, especially when the crash data is unavailable. To identify
traffic conflicts based on traffic flow characteristics across various traffic
states, we propose a framework that utilizes unsupervised learning to
automatically establish surrogate safety measures (SSM) thresholds. Different
traffic states and corresponding transitions are identified with the
three-phase traffic theory using high-resolution trajectory data. Meanwhile,
the SSMs are mapped to the corresponding traffic states from the perspectives
of time, space, and deceleration. Three models, including k-means, GMM, and
Mclust, are investigated and compared to optimize the identification of traffic
conflicts. It is observed that Mclust outperforms the others based on the
evaluation metrics. According to the results, there is a variation in the
distribution of traffic conflicts among different traffic states, wide moving
jam (phase J) has the highest conflict risk, followed by synchronous flow
(phase S), and free flow (phase F). Meanwhile, the thresholds of traffic
conflicts cannot be fully represented by the same value through different
traffic states. It reveals that the heterogeneity of thresholds is exhibited
across traffic state transitions, which justifies the necessity of dynamic
thresholds for traffic conflict analysis.
