Modeling Crashes Severity Using Ensemble Techniques

Taqwa Alhadıdı; Mohammed Elhenawey

doi:10.55549/epstem.1410227

Conference Paper

Year 2023, Volume: 26, 357 - 365, 30.12.2023

Taqwa Alhadıdı Mohammed Elhenawey

https://doi.org/10.55549/epstem.1410227

Abstract

References

Almamlook, R. E., Kwayu, K. M., Alkasisbeh, M. R., & Frefer, A. A. (2019). Comparison of machine learning algorithms for predicting traffic accident severity. IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology JEEIT 2019, 272–276.
Almannaa, M., Zawad, M. N., Moshawah, M., & Alabduljabbar, H. (2023). Investigating the effect of road condition and vacation on crash severity using machine learning algorithms. International Journal of Injury Control and Safety Promotion, 30(3), 392-402.
Al-Mistarehi, B. W., Alomari, A. H., Imam, R., & Mashaqba, M. (2022). Using machine learning models to forecast severity level of traffic crashes by R studio and ArcGIS. Frontiers in Built Environment, 8.

Modeling Crashes Severity Using Ensemble Techniques

Year 2023, Volume: 26, 357 - 365, 30.12.2023

Taqwa Alhadıdı Mohammed Elhenawey

https://doi.org/10.55549/epstem.1410227

Abstract

Traffic crashes are modelled using different techniques and contributing factors. In this work, several ensemble machine learning algorithms were used to model crash severity at urban roundabouts using data from 15 roundabouts in Jordan. The original dataset covers four years, from 2017 to 2021. A total of 15 variables were collected and used in this work. Results indicated that ten variables are important. The various models show their ability to classify traffic crash severity with a high overall accuracy range from 96% to 98%. Results indicated that driver fault and age are the most significant contributing factors for crash severity.

Keywords

Machine learning , K-nearest neighborhood , Support vector machine , Safety , Driver age , driver fault

References

Almamlook, R. E., Kwayu, K. M., Alkasisbeh, M. R., & Frefer, A. A. (2019). Comparison of machine learning algorithms for predicting traffic accident severity. IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology JEEIT 2019, 272–276.
Almannaa, M., Zawad, M. N., Moshawah, M., & Alabduljabbar, H. (2023). Investigating the effect of road condition and vacation on crash severity using machine learning algorithms. International Journal of Injury Control and Safety Promotion, 30(3), 392-402.
Al-Mistarehi, B. W., Alomari, A. H., Imam, R., & Mashaqba, M. (2022). Using machine learning models to forecast severity level of traffic crashes by R studio and ArcGIS. Frontiers in Built Environment, 8.

There are 3 citations in total.

Details

Primary Language	English
Subjects	Environmental and Sustainable Processes
Journal Section	Articles
Authors	Taqwa Alhadıdı Mohammed Elhenawey
Early Pub Date	December 26, 2023
Publication Date	December 30, 2023
Published in Issue	Year 2023 Volume: 26

Cite

APA	Alhadıdı, T., & Elhenawey, M. (2023). Modeling Crashes Severity Using Ensemble Techniques. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 26, 357-365. https://doi.org/10.55549/epstem.1410227