A deep learning approach for robust traffic accident information extraction from online chinese news
Abstract Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognitio...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Intelligent Transport Systems |
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| Online Access: | https://doi.org/10.1049/itr2.12493 |
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| _version_ | 1850265498819756032 |
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| author | Yancheng Ling Zhenliang Ma Xiaoxian Dong Xiaoxiong Weng |
| author_facet | Yancheng Ling Zhenliang Ma Xiaoxian Dong Xiaoxiong Weng |
| author_sort | Yancheng Ling |
| collection | DOAJ |
| description | Abstract Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence‐wise and context‐wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon‐BiLSTM‐CRF method. The proposed approach is validated by comparing it with state‐of‐the‐art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1‐score. It improves the performance of the best benchmark model (BiLSTM‐CRF) by 18.8% in precision and 12.08% in F1‐score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice. |
| format | Article |
| id | doaj-art-9e7064a44cfe4e57ad4d5cf9cdea5327 |
| institution | OA Journals |
| issn | 1751-956X 1751-9578 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj-art-9e7064a44cfe4e57ad4d5cf9cdea53272025-08-20T01:54:25ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-10-0118101847186210.1049/itr2.12493A deep learning approach for robust traffic accident information extraction from online chinese newsYancheng Ling0Zhenliang Ma1Xiaoxian Dong2Xiaoxiong Weng3School of Civil Engineering and Transportation South China University of Technology Guangzhou ChinaDepartment of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm SwedenSchool of Civil Engineering and Transportation South China University of Technology Guangzhou ChinaSchool of Civil Engineering and Transportation South China University of Technology Guangzhou ChinaAbstract Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence‐wise and context‐wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon‐BiLSTM‐CRF method. The proposed approach is validated by comparing it with state‐of‐the‐art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1‐score. It improves the performance of the best benchmark model (BiLSTM‐CRF) by 18.8% in precision and 12.08% in F1‐score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice.https://doi.org/10.1049/itr2.12493artificial intelligenceroad safetytext analysis |
| spellingShingle | Yancheng Ling Zhenliang Ma Xiaoxian Dong Xiaoxiong Weng A deep learning approach for robust traffic accident information extraction from online chinese news IET Intelligent Transport Systems artificial intelligence road safety text analysis |
| title | A deep learning approach for robust traffic accident information extraction from online chinese news |
| title_full | A deep learning approach for robust traffic accident information extraction from online chinese news |
| title_fullStr | A deep learning approach for robust traffic accident information extraction from online chinese news |
| title_full_unstemmed | A deep learning approach for robust traffic accident information extraction from online chinese news |
| title_short | A deep learning approach for robust traffic accident information extraction from online chinese news |
| title_sort | deep learning approach for robust traffic accident information extraction from online chinese news |
| topic | artificial intelligence road safety text analysis |
| url | https://doi.org/10.1049/itr2.12493 |
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