In this paper, a systematic strategy is proposed to identify severe braking events occurrence correlation with time and location. The proposed approach, which is constructed based on batch clustering and real-time clustering techniques, incorporates historical and real-time data to predict the time and location of severe braking events. Batch clustering is implemented with the combination of subtractive clustering and fuzzy c-means clustering to generate clusters representing the initial correlation patterns. Real-time clustering is then developed to create and update real-time correlation patterns on the foundation of the batch clustering using evolving Gustafson Kessel Like (eGKL) algorithm. Real-time driving data of operating vehicles each equipped with a data acquisition and wireless communication platform are used to validate the proposed strategy. Drivers can be notified of the potential severe braking locations through maps, and recognize the events occurrence at different times and locations through the variation of the identified correlation patterns.