AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode Detection
In recent years, with the development of sensor technology and the popularity of smartphones, transportation mode detection has become increasingly integrated into people’s lives, assisting various mobile intelligent services in improving personal health, optimizing urban traffic, etc. Al...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10960293/ |
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| author | Rui Li Xueyi Song Yunmei Wu Xubo Yu Hua Huang |
| author_facet | Rui Li Xueyi Song Yunmei Wu Xubo Yu Hua Huang |
| author_sort | Rui Li |
| collection | DOAJ |
| description | In recent years, with the development of sensor technology and the popularity of smartphones, transportation mode detection has become increasingly integrated into people’s lives, assisting various mobile intelligent services in improving personal health, optimizing urban traffic, etc. Although previous studies have addressed fine-grained transportation mode detection, challenges including complex temporal correlations, non-linearities and interpretability required to further addressed. To this end, we propose a novel attention-KAN (Kolmogorov-Arnold Networks) based deep model for transportation mode detection, which primarily consists of three modules: convolutional KAN, multi-head attention and linear KAN. By introducing learnable B-spline functions, KAN can express more abundant feature information with fewer parameters which strengthens the non-linearities modeling and interpretability. Specifically, we employ convolutional KAN, which combines the advantages of KAN and convolutional neural network (CNN) to capture the short-term temporal correlations for distinct sensor features. Then we exploit multi-head attention to effectively capture long-term temporal correlations for linked sensor features in parallel, resulting in better trade-off between accuracy and efficiency. Extensive experimental results demonstrate that our proposed AKTMD model outperforms the state-of-the-art TMD-BERT model among the compared baselines, achieving an accuracy of 98.12% on the SHL dataset and 95.52% on the HTC dataset, with improvements of 5.05% and 3.29% over the TMD-BERT model on each dataset. |
| format | Article |
| id | doaj-art-5d2af437e8fe453cb3d523ced3c89bfe |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5d2af437e8fe453cb3d523ced3c89bfe2025-08-20T03:18:24ZengIEEEIEEE Access2169-35362025-01-0113636906370210.1109/ACCESS.2025.355941710960293AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode DetectionRui Li0https://orcid.org/0009-0001-9529-9265Xueyi Song1https://orcid.org/0009-0008-9842-4739Yunmei Wu2https://orcid.org/0009-0005-7515-7162Xubo Yu3https://orcid.org/0000-0003-0420-7255Hua Huang4https://orcid.org/0009-0008-5081-8325Zhejiang Technical Institute of Economics, Hangzhou, Zhejiang, ChinaChina Construction Civil Engineering Company Ltd., Beijing, ChinaZhejiang Technical Institute of Economics, Hangzhou, Zhejiang, ChinaThe Institute of Service-Oriented Manufacturing, Hangzhou, Zhejiang, ChinaZhejiang Technical Institute of Economics, Hangzhou, Zhejiang, ChinaIn recent years, with the development of sensor technology and the popularity of smartphones, transportation mode detection has become increasingly integrated into people’s lives, assisting various mobile intelligent services in improving personal health, optimizing urban traffic, etc. Although previous studies have addressed fine-grained transportation mode detection, challenges including complex temporal correlations, non-linearities and interpretability required to further addressed. To this end, we propose a novel attention-KAN (Kolmogorov-Arnold Networks) based deep model for transportation mode detection, which primarily consists of three modules: convolutional KAN, multi-head attention and linear KAN. By introducing learnable B-spline functions, KAN can express more abundant feature information with fewer parameters which strengthens the non-linearities modeling and interpretability. Specifically, we employ convolutional KAN, which combines the advantages of KAN and convolutional neural network (CNN) to capture the short-term temporal correlations for distinct sensor features. Then we exploit multi-head attention to effectively capture long-term temporal correlations for linked sensor features in parallel, resulting in better trade-off between accuracy and efficiency. Extensive experimental results demonstrate that our proposed AKTMD model outperforms the state-of-the-art TMD-BERT model among the compared baselines, achieving an accuracy of 98.12% on the SHL dataset and 95.52% on the HTC dataset, with improvements of 5.05% and 3.29% over the TMD-BERT model on each dataset.https://ieeexplore.ieee.org/document/10960293/Transportation mode detection (TMD)sensorsKolmogorov-Arnold Networks (KAN)convolutional KANmulti-head attention |
| spellingShingle | Rui Li Xueyi Song Yunmei Wu Xubo Yu Hua Huang AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode Detection IEEE Access Transportation mode detection (TMD) sensors Kolmogorov-Arnold Networks (KAN) convolutional KAN multi-head attention |
| title | AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode Detection |
| title_full | AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode Detection |
| title_fullStr | AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode Detection |
| title_full_unstemmed | AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode Detection |
| title_short | AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode Detection |
| title_sort | aktmd attention kan based neural networks for transportation mode detection |
| topic | Transportation mode detection (TMD) sensors Kolmogorov-Arnold Networks (KAN) convolutional KAN multi-head attention |
| url | https://ieeexplore.ieee.org/document/10960293/ |
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