NDLI: Cluster-based correlation of severe braking events with time and location

Content Provider	IEEE Xplore Digital Library
Author	Guoyan Cao Michelini, J. Grigoriadis, K. Ebrahimi, B. Franchek, M.A.
Copyright Year	2015
Abstract	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.
Starting Page	187
Ending Page	192
File Size	1644137
Page Count	6
File Format	PDF
ISBN	9781479976119
DOI	10.1109/SYSOSE.2015.7151986
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Real-time systems Vehicles Clustering algorithms Covariance matrices Systems engineering and theory Data acquisition evolving Gustafson Kessel Like approach correlation identification severe driving events clustering
Content Type	Text
Resource Type	Article

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