Guardians of the road: machine learning solutions for safer commutes

Qiao Peng*, Honghao He, Ying Gao, Taicheng Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceedings of the Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024)
PublisherSPIE - The International Society for Optical Engineering
DOIs
Publication statusPublished - 16 May 2024
Event2024 4th International Conference on Smart City Engineering and Public Transportation (SCEPT 2024) | -
Duration: 26 Jan 202428 Jan 2024

Publication series

NameSPIE Conference Proceedings
Volume13160
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2024 4th International Conference on Smart City Engineering and Public Transportation (SCEPT 2024) |
Period26/01/202428/01/2024

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