Abstract
Accurate identification of aggressive driving behavior is crucial for improving traffic safety and promoting social responsibility, and it is also a key component in the realization of intelligent transportation systems. This paper proposes a method for aggressive driving behavior recognition and data modeling based on K-means clustering. First, the NGSIM (Next Generation Simulation) vehicle trajectory dataset is used for preprocessing the raw data, and effective behavioral feature representations are obtained through feature extraction. Next, Principal Component Analysis (PCA) is applied to reduce the dimensionality of the data, extracting three main components to simplify the data structure. Subsequently, the K-means clustering algorithm is employed to classify the reduced data and identify potential patterns of aggressive driving behavior. Experimental results demonstrate that the proposed method effectively identifies aggressive driving behavior, providing technical support for improving traffic system safety and raising awareness of social responsibility.
Published Version
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