Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset
Road traffic crashes have increased over the years leading to greater injury severity among children who are mostly vehicle occupants in high-income countries. This adversely affects the healthy development of children and might lead to death. However, studies in the literature have focused on predi...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | IATSS Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0386111223000249 |
| _version_ | 1797787278037221376 |
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| author | Muhammad Uba Abdulazeez Wasif Khan Kassim Abdulrahman Abdullah |
| author_facet | Muhammad Uba Abdulazeez Wasif Khan Kassim Abdulrahman Abdullah |
| author_sort | Muhammad Uba Abdulazeez |
| collection | DOAJ |
| description | Road traffic crashes have increased over the years leading to greater injury severity among children who are mostly vehicle occupants in high-income countries. This adversely affects the healthy development of children and might lead to death. However, studies in the literature have focused on predicting crash injuries among adults while children have different crash injury risks as well as crash kinematics compared to adults. To address this gap, this paper presents a new dataset for child occupant crash injury severity prediction collected over 8 years (2012 to 2019) in the United Arab Emirates (UAE). The performance of state-of-the-art machine learning algorithms was then evaluated using the proposed dataset. In addition, feature selection techniques and logistic regression model were employed to extract the most significant features for crash injury severity prediction among child occupants. Furthermore, the impact of data balancing approaches on the prediction performance was analyzed as the dataset is highly imbalanced. The experimental results showed that Adaboost, Bagging REP, ZeroR, OneR, and Decision Table algorithms predicts child occupant injury severity with the highest accuracy. Child occupant seating position, emirate, crash location, crash type and crash cause were observed as significant features that predicts injury severity by both the feature selection and logistic regression models. |
| first_indexed | 2024-03-13T01:19:44Z |
| format | Article |
| id | doaj.art-b325117f1ff4443eade9dcbe1e78dd41 |
| institution | Directory Open Access Journal |
| issn | 0386-1112 |
| language | English |
| last_indexed | 2024-03-13T01:19:44Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj.art-b325117f1ff4443eade9dcbe1e78dd412023-07-05T05:14:34ZengElsevierIATSS Research0386-11122023-07-01472134159Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced datasetMuhammad Uba Abdulazeez0Wasif Khan1Kassim Abdulrahman Abdullah2Department of Mechanical & Aerospace Engineering, College of Engineering, United Arab Emirates University, P.O. Box 15551, Al Ain, Abu Dhabi, United Arab Emirates; Emirates Center for Mobility Research, United Arab Emirates University, P.O. Box 15551, Al Ain, Abu Dhabi, United Arab Emirates; Department of Automotive Engineering, Faculty of Engineering and Engineering Technology, Abubakar Tafawa Balewa University, P.M.B 0248 Bauchi, NigeriaDepartment of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, Abu Dhabi, United Arab Emirates; Big Data Analytics Center, United Arab Emirates University, P.O. Box 15551, Al Ain, Abu Dhabi, United Arab EmiratesDepartment of Mechanical & Aerospace Engineering, College of Engineering, United Arab Emirates University, P.O. Box 15551, Al Ain, Abu Dhabi, United Arab Emirates; Emirates Center for Mobility Research, United Arab Emirates University, P.O. Box 15551, Al Ain, Abu Dhabi, United Arab Emirates; Corresponding author at: United Arab Emirates University, Sheikh Khalifa Bin Zayed Street, Al Ain, Abu Dhabi 15551, UAE.Road traffic crashes have increased over the years leading to greater injury severity among children who are mostly vehicle occupants in high-income countries. This adversely affects the healthy development of children and might lead to death. However, studies in the literature have focused on predicting crash injuries among adults while children have different crash injury risks as well as crash kinematics compared to adults. To address this gap, this paper presents a new dataset for child occupant crash injury severity prediction collected over 8 years (2012 to 2019) in the United Arab Emirates (UAE). The performance of state-of-the-art machine learning algorithms was then evaluated using the proposed dataset. In addition, feature selection techniques and logistic regression model were employed to extract the most significant features for crash injury severity prediction among child occupants. Furthermore, the impact of data balancing approaches on the prediction performance was analyzed as the dataset is highly imbalanced. The experimental results showed that Adaboost, Bagging REP, ZeroR, OneR, and Decision Table algorithms predicts child occupant injury severity with the highest accuracy. Child occupant seating position, emirate, crash location, crash type and crash cause were observed as significant features that predicts injury severity by both the feature selection and logistic regression models.http://www.sciencedirect.com/science/article/pii/S0386111223000249Crash injury severityChild occupantMachine learningData balancingFeature selectionInjury severity prediction |
| spellingShingle | Muhammad Uba Abdulazeez Wasif Khan Kassim Abdulrahman Abdullah Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset IATSS Research Crash injury severity Child occupant Machine learning Data balancing Feature selection Injury severity prediction |
| title | Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset |
| title_full | Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset |
| title_fullStr | Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset |
| title_full_unstemmed | Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset |
| title_short | Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset |
| title_sort | predicting child occupant crash injury severity in the united arab emirates using machine learning models for imbalanced dataset |
| topic | Crash injury severity Child occupant Machine learning Data balancing Feature selection Injury severity prediction |
| url | http://www.sciencedirect.com/science/article/pii/S0386111223000249 |
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