Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data
This study aims to develop a machine learning-based framework for predicting the severity of highway traffic accidents by leveraging high-resolution data from Taiwan’s Electronic Toll Collection (ETC) system. Unlike traditional accident-reporting systems, the ETC infrastructure provides a uniquely c...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/atr/8468192 |
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| _version_ | 1850034038891347968 |
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| author | Pei-Chun Lin Kuan-Yen Chen Jenhung Wang |
| author_facet | Pei-Chun Lin Kuan-Yen Chen Jenhung Wang |
| author_sort | Pei-Chun Lin |
| collection | DOAJ |
| description | This study aims to develop a machine learning-based framework for predicting the severity of highway traffic accidents by leveraging high-resolution data from Taiwan’s Electronic Toll Collection (ETC) system. Unlike traditional accident-reporting systems, the ETC infrastructure provides a uniquely comprehensive and precise dataset that captures spatiotemporal traffic patterns and environmental conditions across the national highway network. This rich dataset enabled the integration of data mining and data visualization techniques to uncover nontypical contributing factors to accident severity. Feature engineering was conducted using random forest and LASSO regression, while extreme gradient boosting and the Apriori algorithm were employed to identify key associations between accident severity and contextual variables. Based on human factor and traffic psychology theory, influential factors include poor lighting at night, adverse weather conditions, late-night hours (20:00–06:00), specific geographic regions (e.g., Yilan County), speed limits of 100 km/h, and vehicle types such as taxis and large trucks. The findings not only enhance the understanding of environmental influences on accident outcomes but also offer actionable insights for improving highway safety. Moreover, Taiwan’s ETC system serves as a model for countries seeking to integrate tolling infrastructure with traffic safety analytics. |
| format | Article |
| id | doaj-art-7c68cc825fec42f78b82f83ab4563f7d |
| institution | DOAJ |
| issn | 2042-3195 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7c68cc825fec42f78b82f83ab4563f7d2025-08-20T02:57:57ZengWileyJournal of Advanced Transportation2042-31952025-01-01202510.1155/atr/8468192Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) DataPei-Chun Lin0Kuan-Yen Chen1Jenhung Wang2Department of Transportation and Communication Management ScienceDepartment of Transportation and Communication Management ScienceDepartment of Logistics ManagementThis study aims to develop a machine learning-based framework for predicting the severity of highway traffic accidents by leveraging high-resolution data from Taiwan’s Electronic Toll Collection (ETC) system. Unlike traditional accident-reporting systems, the ETC infrastructure provides a uniquely comprehensive and precise dataset that captures spatiotemporal traffic patterns and environmental conditions across the national highway network. This rich dataset enabled the integration of data mining and data visualization techniques to uncover nontypical contributing factors to accident severity. Feature engineering was conducted using random forest and LASSO regression, while extreme gradient boosting and the Apriori algorithm were employed to identify key associations between accident severity and contextual variables. Based on human factor and traffic psychology theory, influential factors include poor lighting at night, adverse weather conditions, late-night hours (20:00–06:00), specific geographic regions (e.g., Yilan County), speed limits of 100 km/h, and vehicle types such as taxis and large trucks. The findings not only enhance the understanding of environmental influences on accident outcomes but also offer actionable insights for improving highway safety. Moreover, Taiwan’s ETC system serves as a model for countries seeking to integrate tolling infrastructure with traffic safety analytics.http://dx.doi.org/10.1155/atr/8468192 |
| spellingShingle | Pei-Chun Lin Kuan-Yen Chen Jenhung Wang Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data Journal of Advanced Transportation |
| title | Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data |
| title_full | Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data |
| title_fullStr | Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data |
| title_full_unstemmed | Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data |
| title_short | Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data |
| title_sort | predicting accident severity on taiwan highways using machine learning and electronic toll collection etc data |
| url | http://dx.doi.org/10.1155/atr/8468192 |
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