Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study
A better understanding of injury severity risk factors is fundamental to improving crash prediction and effective implementation of appropriate mitigation strategies. Traditional statistical models widely used in this regard have predefined correlation and intrinsic assumptions, which, if flouted, may yield biased predictions. The present study investigates the possibility of using the eXtreme Gradient Boosting (XGBoost) model compared with few traditional machine learning algorithms (logistic regression, random forest, and decision tree) for crash injury severity analysis. The data used in this study was obtained from the traffic safety department, ministry of transport (MOT) at Riyadh, KSA, and contains 13,546 motor vehicle collisions along 15 rural highways reported between January 2017 to December 2019. Empirical results obtained using k-fold (k = 10) for various performance metrics showed that the XGBoost technique outperformed other models in terms of the collective predictive performance as well as injury severity individual class accuracies. XGBoost feature importance analysis indicated that collision type, weather status, road surface conditions, on-site damage type, lighting conditions, and vehicle type are the few sensitive variables in predicting the crash injury severity outcome. Finally, a comparative analysis of XGBoost based on different performance statistics showed that the authors' model outperformed most previous studies.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/17457300
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Supplemental Notes:
- © 2021 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Jamal, Arshad
- Zahid, Muhammad
- Tauhidur Rahman, Muhammad
- Al-Ahmadi, Hassan M
- Almoshaogeh, Meshal
- Farooq, Danish
- Ahmad, Mahmood
- Publication Date: 2021-10
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 408-427
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Serial:
- International Journal of Injury Control and Safety Promotion
- Volume: 28
- Issue Number: 4
- Publisher: Taylor & Francis
- ISSN: 1745-7300
- EISSN: 1745-7319
- Serial URL: http://www.tandfonline.com/loi/nics20
Subject/Index Terms
- TRT Terms: Alternatives analysis; Crash analysis; Injury severity; Machine learning; Mathematical prediction; Sensitivity analysis; Traffic crashes
- Geographic Terms: Riyadh (Saudi Arabia)
- Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;
Filing Info
- Accession Number: 01835198
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 1 2022 11:06AM