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.
Details
| Primary Language | English |
|---|---|
| Subjects | Environmental and Sustainable Processes |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 26, 2023 |
| Publication Date | December 30, 2023 |
| Published in Issue | Year 2023Volume: 26 |
