PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors - Ning Ma; Kaijun Wu; Yubin Yuan; Jiawei Li; Xiaoqiang Wu (Alexandria Engineering Journal)
Fuzzy clustering algorithms are widely applied in the field of traffic driving, aiding in the classification of driving behaviors from massive traffic data and enhancing traffic safety levels. However, classical Fuzzy C-Means (FCM) algorithms are sensitive to noise during the clustering process, leading to suboptimal performance when dealing with traffic datasets with lower accuracy. Moreover, single-kernel clustering algorithms are greatly influenced by kernel function selection. To address these issues, this paper proposes a Possibility Weighted Multi-Kernel Fuzzy Clustering Algorithm (PMWFCM). By integrating possibility-based fuzzy clustering with FCM and introducing a multi-kernel weighting mechanism, PMWFCM effectively reduces FCM’s sensitivity to outliers while resolving issues of clustering consistency in Possibilistic C-Means (PCM) algorithms, overcoming the challenges associated with kernel function selection. Validation on three different types of datasets demonstrates that the PMWFCM algorithm performs exceptionally well in terms of average accuracy, normalized information, average time, robustness, and convergence. When applied to the evaluation of driving behaviors in traffic datasets. Therefore, the improved FCM algorithm proposed in this paper can accurately and comprehensively reflect changes in traffic data, providing a solid theoretical foundation for identifying and assessing major risk types among passenger drivers.