{"title":"利用机器学习算法模拟驾驶员受伤严重程度","authors":"Neero Gumsar Sorum, Dibyendu Pal","doi":"10.1139/cjce-2023-0503","DOIUrl":null,"url":null,"abstract":"This study planned to predict and analyze the driver injury severity (DIS) using twelve machine learning (ML) algorithms. Police reports of single and two-vehicle accidents that occurred during 2011–2020 in the two cities of India (Itanagar and Imphal) were used in this study. The best-performing model to predict the DIS for Itanagar was Gradient Boosting Trees (GBT). ‘Causes of Accident’ variable had shown maximum impact on the DIS. In the case of Imphal, it was the GBT, Extra Trees, and Random Forest models across all k-fold cross-validation for train ratios 0.70, 0.80, and 0.90, respectively. ‘Causes of Accident’, and ‘Vehicle Type’ had shown maximum impact on the DIS. These results reveal that the ML models can be applied in hilly areas to predict and identify the important factors that affect DIS. Transportation authorities can analyze road accident data using these models while implementing various road safety measures.","PeriodicalId":9414,"journal":{"name":"Canadian Journal of Civil Engineering","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling Driver Injury Severity using Machine Learning Algorithms\",\"authors\":\"Neero Gumsar Sorum, Dibyendu Pal\",\"doi\":\"10.1139/cjce-2023-0503\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study planned to predict and analyze the driver injury severity (DIS) using twelve machine learning (ML) algorithms. Police reports of single and two-vehicle accidents that occurred during 2011–2020 in the two cities of India (Itanagar and Imphal) were used in this study. The best-performing model to predict the DIS for Itanagar was Gradient Boosting Trees (GBT). ‘Causes of Accident’ variable had shown maximum impact on the DIS. In the case of Imphal, it was the GBT, Extra Trees, and Random Forest models across all k-fold cross-validation for train ratios 0.70, 0.80, and 0.90, respectively. ‘Causes of Accident’, and ‘Vehicle Type’ had shown maximum impact on the DIS. These results reveal that the ML models can be applied in hilly areas to predict and identify the important factors that affect DIS. Transportation authorities can analyze road accident data using these models while implementing various road safety measures.\",\"PeriodicalId\":9414,\"journal\":{\"name\":\"Canadian Journal of Civil Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Journal of Civil Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1139/cjce-2023-0503\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Civil Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1139/cjce-2023-0503","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Modeling Driver Injury Severity using Machine Learning Algorithms
This study planned to predict and analyze the driver injury severity (DIS) using twelve machine learning (ML) algorithms. Police reports of single and two-vehicle accidents that occurred during 2011–2020 in the two cities of India (Itanagar and Imphal) were used in this study. The best-performing model to predict the DIS for Itanagar was Gradient Boosting Trees (GBT). ‘Causes of Accident’ variable had shown maximum impact on the DIS. In the case of Imphal, it was the GBT, Extra Trees, and Random Forest models across all k-fold cross-validation for train ratios 0.70, 0.80, and 0.90, respectively. ‘Causes of Accident’, and ‘Vehicle Type’ had shown maximum impact on the DIS. These results reveal that the ML models can be applied in hilly areas to predict and identify the important factors that affect DIS. Transportation authorities can analyze road accident data using these models while implementing various road safety measures.
期刊介绍:
The Canadian Journal of Civil Engineering is the official journal of the Canadian Society for Civil Engineering. It contains articles on environmental engineering, hydrotechnical engineering, structural engineering, construction engineering, engineering mechanics, engineering materials, and history of civil engineering. Contributors include recognized researchers and practitioners in industry, government, and academia. New developments in engineering design and construction are also featured.