PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors
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, lea...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014959 |
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| author | Ning Ma Kaijun Wu Yubin Yuan Jiawei Li Xiaoqiang Wu |
| author_facet | Ning Ma Kaijun Wu Yubin Yuan Jiawei Li Xiaoqiang Wu |
| author_sort | Ning Ma |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-43cb12dcf3a14505be449c45f340a853 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-43cb12dcf3a14505be449c45f340a8532025-02-07T04:47:07ZengElsevierAlexandria Engineering Journal1110-01682025-02-01113249261PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviorsNing Ma0Kaijun Wu1Yubin Yuan2Jiawei Li3Xiaoqiang Wu4School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China; Corresponding author.School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, ChinaSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, ChinaSchool of Mechanical and Traffic Engineering, Ordos Institute of Technology, Ordos, 017000, ChinaFuzzy 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.http://www.sciencedirect.com/science/article/pii/S1110016824014959Fuzzy clustering algorithmPossibilistic fuzzy clusteringKernel functionTraffic driving assessment |
| spellingShingle | Ning Ma Kaijun Wu Yubin Yuan Jiawei Li Xiaoqiang Wu PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors Alexandria Engineering Journal Fuzzy clustering algorithm Possibilistic fuzzy clustering Kernel function Traffic driving assessment |
| title | PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors |
| title_full | PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors |
| title_fullStr | PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors |
| title_full_unstemmed | PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors |
| title_short | PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors |
| title_sort | pmwfcm a possibility based multikernel weighted fuzzy clustering algorithm for classification of driving behaviors |
| topic | Fuzzy clustering algorithm Possibilistic fuzzy clustering Kernel function Traffic driving assessment |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824014959 |
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