Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning
Traffic accidents occur frequently, causing significant losses to people’s lives and property safety. Accurately predicting the severity level of traffic accidents is of great significance. Based on traffic accident data, this study comprehensively considers various influencing factors such as the g...
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MDPI AG
2025-01-01
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| author | Jiming Tang Yao Huang Dingli Liu Liuyuan Xiong Rongwei Bu |
| author_facet | Jiming Tang Yao Huang Dingli Liu Liuyuan Xiong Rongwei Bu |
| author_sort | Jiming Tang |
| collection | DOAJ |
| description | Traffic accidents occur frequently, causing significant losses to people’s lives and property safety. Accurately predicting the severity level of traffic accidents is of great significance. Based on traffic accident data, this study comprehensively considers various influencing factors such as the geographical location, road conditions, and environment. The data are divided into accident-related categories, weather-related categories, and road- and environment-related categories. The machine learning method is improved through integration for the accident level prediction. In the experiment, effective preprocessing measures were taken for problems such as data imbalance, missing values, the encoding of categorical variables, and the standardization of numerical features. The unbalanced distribution of “Severity” was improved through under-sampling and over-sampling techniques. Firstly, we adopted a multi-stage fusion strategy. A multi-layer perceptron (MLP) was used for the preliminary prediction, and then its result was combined with the original features to form a new feature. Decision tree, XGBoost, and random forest algorithms, respectively, were applied for the secondary prediction. The analysis results show that the improved machine learning model is significantly superior to a single model in the overall performance. The “MLP + random forest” model performs well in evaluation indicators such as the accuracy, recall rate, and F1 value. The accuracy rate is as high as 94%. In the prediction of different traffic accident severity levels (minor, moderate, and severe), the improved machine learning model also generally shows better performance and stability. The research results of this study have broad prospects in the field of intelligent driving. It can realize real-time accident prediction and early warnings, and provide decision support for drivers and autonomous driving systems. The research also provides a scientific basis for traffic planning and management departments to improve driving conditions and reduce the probability and losses of traffic accidents. |
| format | Article |
| id | doaj-art-f63c7a50ebb34004aecad186c76e52b8 |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Systems |
| spelling | doaj-art-f63c7a50ebb34004aecad186c76e52b82025-01-24T13:50:33ZengMDPI AGSystems2079-89542025-01-011313110.3390/systems13010031Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine LearningJiming Tang0Yao Huang1Dingli Liu2Liuyuan Xiong3Rongwei Bu4School of Traffic and Transportation Engineering, Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety of Ministry of Education, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Traffic and Transportation Engineering, Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety of Ministry of Education, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Traffic and Transportation Engineering, Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety of Ministry of Education, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Traffic and Transportation Engineering, Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety of Ministry of Education, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Traffic and Transportation Engineering, Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road & Traffic Safety of Ministry of Education, Changsha University of Science and Technology, Changsha 410114, ChinaTraffic accidents occur frequently, causing significant losses to people’s lives and property safety. Accurately predicting the severity level of traffic accidents is of great significance. Based on traffic accident data, this study comprehensively considers various influencing factors such as the geographical location, road conditions, and environment. The data are divided into accident-related categories, weather-related categories, and road- and environment-related categories. The machine learning method is improved through integration for the accident level prediction. In the experiment, effective preprocessing measures were taken for problems such as data imbalance, missing values, the encoding of categorical variables, and the standardization of numerical features. The unbalanced distribution of “Severity” was improved through under-sampling and over-sampling techniques. Firstly, we adopted a multi-stage fusion strategy. A multi-layer perceptron (MLP) was used for the preliminary prediction, and then its result was combined with the original features to form a new feature. Decision tree, XGBoost, and random forest algorithms, respectively, were applied for the secondary prediction. The analysis results show that the improved machine learning model is significantly superior to a single model in the overall performance. The “MLP + random forest” model performs well in evaluation indicators such as the accuracy, recall rate, and F1 value. The accuracy rate is as high as 94%. In the prediction of different traffic accident severity levels (minor, moderate, and severe), the improved machine learning model also generally shows better performance and stability. The research results of this study have broad prospects in the field of intelligent driving. It can realize real-time accident prediction and early warnings, and provide decision support for drivers and autonomous driving systems. The research also provides a scientific basis for traffic planning and management departments to improve driving conditions and reduce the probability and losses of traffic accidents.https://www.mdpi.com/2079-8954/13/1/31traffic accidentseverityimproved machine learningprediction modelsampling technique |
| spellingShingle | Jiming Tang Yao Huang Dingli Liu Liuyuan Xiong Rongwei Bu Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning Systems traffic accident severity improved machine learning prediction model sampling technique |
| title | Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning |
| title_full | Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning |
| title_fullStr | Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning |
| title_full_unstemmed | Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning |
| title_short | Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning |
| title_sort | research on traffic accident severity level prediction model based on improved machine learning |
| topic | traffic accident severity improved machine learning prediction model sampling technique |
| url | https://www.mdpi.com/2079-8954/13/1/31 |
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