Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks
The number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most current research focuses on analyzing the cau...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/23/12963 |
| _version_ | 1797400374468935680 |
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| author | Yongdong Wang Haonan Zhai Xianghong Cao Xin Geng |
| author_facet | Yongdong Wang Haonan Zhai Xianghong Cao Xin Geng |
| author_sort | Yongdong Wang |
| collection | DOAJ |
| description | The number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most current research focuses on analyzing the causes of traffic accidents rather than investigating the underlying factors. This study creates a complex network for road traffic accident cause analysis using the topology method for complex networks. The network metrics are analyzed using the network parameters to obtain reduced dimensionality feature factors, and four machine learning techniques are applied to accurately classify the accidents’ severity based on the analysis results. The study divides real traffic accident data into three main categories based on the factors that influences them: time, environment, and traffic management. The results show that traffic management factors have the most significant impact on road accidents. The study also finds that Extreme Gradient Boosting (XGBoost) outperforms Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) in accurately categorizing the severity of traffic accidents. |
| first_indexed | 2024-03-09T01:54:42Z |
| format | Article |
| id | doaj.art-4710ec110c584ba5a0643a1d5027f35d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:54:42Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4710ec110c584ba5a0643a1d5027f35d2023-12-08T15:12:18ZengMDPI AGApplied Sciences2076-34172023-12-0113231296310.3390/app132312963Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex NetworksYongdong Wang0Haonan Zhai1Xianghong Cao2Xin Geng3The School of Building Environmental Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaThe School of Building Environmental Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaThe School of Building Environmental Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaThe School of Building Environmental Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaThe number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most current research focuses on analyzing the causes of traffic accidents rather than investigating the underlying factors. This study creates a complex network for road traffic accident cause analysis using the topology method for complex networks. The network metrics are analyzed using the network parameters to obtain reduced dimensionality feature factors, and four machine learning techniques are applied to accurately classify the accidents’ severity based on the analysis results. The study divides real traffic accident data into three main categories based on the factors that influences them: time, environment, and traffic management. The results show that traffic management factors have the most significant impact on road accidents. The study also finds that Extreme Gradient Boosting (XGBoost) outperforms Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) in accurately categorizing the severity of traffic accidents.https://www.mdpi.com/2076-3417/13/23/12963road traffic accidentscomplex networkcause analysisfeature dimensionality reductionmachine learning |
| spellingShingle | Yongdong Wang Haonan Zhai Xianghong Cao Xin Geng Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks Applied Sciences road traffic accidents complex network cause analysis feature dimensionality reduction machine learning |
| title | Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks |
| title_full | Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks |
| title_fullStr | Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks |
| title_full_unstemmed | Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks |
| title_short | Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks |
| title_sort | cause analysis and accident classification of road traffic accidents based on complex networks |
| topic | road traffic accidents complex network cause analysis feature dimensionality reduction machine learning |
| url | https://www.mdpi.com/2076-3417/13/23/12963 |
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