An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm
Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These mode...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/1257627 |
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| _version_ | 1832566046518673408 |
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| author | Jing Gan Linheng Li Dapeng Zhang Ziwei Yi Qiaojun Xiang |
| author_facet | Jing Gan Linheng Li Dapeng Zhang Ziwei Yi Qiaojun Xiang |
| author_sort | Jing Gan |
| collection | DOAJ |
| description | Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety. |
| format | Article |
| id | doaj-art-170400fde6ef43f2beccb64a2f0a31a7 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-170400fde6ef43f2beccb64a2f0a31a72025-02-03T01:05:15ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/12576271257627An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests AlgorithmJing Gan0Linheng Li1Dapeng Zhang2Ziwei Yi3Qiaojun Xiang4Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, ChinaJiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, ChinaBig Data Research Center, Southwestern University of Finance and Economics, Chengdu 611130, ChinaJiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, ChinaJiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, ChinaTraffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety.http://dx.doi.org/10.1155/2020/1257627 |
| spellingShingle | Jing Gan Linheng Li Dapeng Zhang Ziwei Yi Qiaojun Xiang An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm Journal of Advanced Transportation |
| title | An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm |
| title_full | An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm |
| title_fullStr | An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm |
| title_full_unstemmed | An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm |
| title_short | An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm |
| title_sort | alternative method for traffic accident severity prediction using deep forests algorithm |
| url | http://dx.doi.org/10.1155/2020/1257627 |
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