Guardians of the road: machine learning solutions for safer commutes

Qiao Peng; Honghao He; Ying Gao; Taicheng Zhang

doi:10.1117/12.3030614

16 May 2024 Guardians of the road: machine learning solutions for safer commutes

Qiao Peng, Honghao He, Ying Gao, Taicheng Zhang

Proceedings Volume 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024); 131601D (2024) https://doi.org/10.1117/12.3030614
Event: 4th International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 2024, Beijin, China

Abstract

Road Traffic Accidents (RTAs) are a serious safety issue, especially in fast-growing cities, and have become one of the leading causes of death worldwide. This study takes Addis Ababa, Ethiopia, as a case study for the period from 2017 to 2020 and uses advanced interpretable machine learning techniques to analyse the key features that influence road safety. The results highlight the superior performance of the Random Forest model. Interestingly, findings indicate that a large number of accidents occurred under normal road and weather conditions, highlighting the significant influence of driver characteristics. This study provides relevant authorities with effective strategies to significantly reduce mortality in persistent RTAs.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiao Peng, Honghao He, Ying Gao, and Taicheng Zhang "Guardians of the road: machine learning solutions for safer commutes", Proc. SPIE 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 131601D (16 May 2024); https://doi.org/10.1117/12.3030614

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