Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety
To monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the road using artificial intelligence or machine lea...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/17/4671 |
| _version_ | 1797557113392726016 |
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| author | Shengda Luo Alex Po Leung Xingzhao Qiu Jan Y. K. Chan Haozhi Huang |
| author_facet | Shengda Luo Alex Po Leung Xingzhao Qiu Jan Y. K. Chan Haozhi Huang |
| author_sort | Shengda Luo |
| collection | DOAJ |
| description | To monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the road using artificial intelligence or machine learning techniques for big data analytics has become a possibility recently. Due to the high false classification rates of the current methods, our goal is to build a practical and accurate method for road safety predictions that automatically determine if the driving behaviour is safe on public transportation. In this paper, our main contributions include (1) a novel feature extraction method because of the lack of informative features in raw CAN bus data, (2) a novel boosting method for driving behaviour classification (safe or unsafe) to combine advantages of deep learning and shallow learning methods with much improved performance, and (3) an evaluation of our method using a real-world data to provide accurate labels from domain experts in the public transportation industry for the first time. The experiments show that the proposed boosting method with our proposed features outperforms seven other popular methods on the real-world dataset by 5.9% and 5.5%. |
| first_indexed | 2024-03-10T17:12:35Z |
| format | Article |
| id | doaj.art-f35d58275ade40078d3b604b753c80b4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:12:35Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f35d58275ade40078d3b604b753c80b42023-11-20T10:37:50ZengMDPI AGSensors1424-82202020-08-012017467110.3390/s20174671Complementary Deep and Shallow Learning with Boosting for Public Transportation SafetyShengda Luo0Alex Po Leung1Xingzhao Qiu2Jan Y. K. Chan3Haozhi Huang4Faculty of Information Technology, Macau University of Science and Technology, Taipa 999078, MacaoFaculty of Information Technology, Macau University of Science and Technology, Taipa 999078, MacaoFaculty of Information Technology, Macau University of Science and Technology, Taipa 999078, MacaoFaculty of Information Technology, Macau University of Science and Technology, Taipa 999078, MacaoFaculty of Information Technology, Macau University of Science and Technology, Taipa 999078, MacaoTo monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the road using artificial intelligence or machine learning techniques for big data analytics has become a possibility recently. Due to the high false classification rates of the current methods, our goal is to build a practical and accurate method for road safety predictions that automatically determine if the driving behaviour is safe on public transportation. In this paper, our main contributions include (1) a novel feature extraction method because of the lack of informative features in raw CAN bus data, (2) a novel boosting method for driving behaviour classification (safe or unsafe) to combine advantages of deep learning and shallow learning methods with much improved performance, and (3) an evaluation of our method using a real-world data to provide accurate labels from domain experts in the public transportation industry for the first time. The experiments show that the proposed boosting method with our proposed features outperforms seven other popular methods on the real-world dataset by 5.9% and 5.5%.https://www.mdpi.com/1424-8220/20/17/4671controller area networktransportationdeep learningmachine learning |
| spellingShingle | Shengda Luo Alex Po Leung Xingzhao Qiu Jan Y. K. Chan Haozhi Huang Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety Sensors controller area network transportation deep learning machine learning |
| title | Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety |
| title_full | Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety |
| title_fullStr | Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety |
| title_full_unstemmed | Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety |
| title_short | Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety |
| title_sort | complementary deep and shallow learning with boosting for public transportation safety |
| topic | controller area network transportation deep learning machine learning |
| url | https://www.mdpi.com/1424-8220/20/17/4671 |
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