| Issue |
E3S Web Conf.
Volume 418, 2023
African Cities Conference (ACC 2023): A part of African Cities Lab 2023 Summit
|
|
|---|---|---|
| Article Number | 02004 | |
| Number of page(s) | 4 | |
| Section | Smart and Sustainable Urban Mobility and Transportation | |
| DOI | https://doi.org/10.1051/e3sconf/202341802004 | |
| Published online | 18 August 2023 | |
A Quantitative Approach to Road Safety in Morocco: Reducing Accidents through Predictive Modeling
1 Laboratory ADMIR, Higher National School of Computer Science and System Analysis (ENSIAS), University of Mohamed V, Rabat, Morocco
2 IEVIA team, IMAGE laboratory, Department of Sciences, ENS, Moulay Ismail University of Meknes, Morocco
3 Center of Urban Systems (CUS), Mohamed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, 43150, Ben Guerir, Morocco.
4 LITE, Department of Computer and Educational Technology, Higher Teacher Training College, University of Yaoundé 1, Yaoundé, Cameroon.
5 Moroccan Foundation for Advanced Science Innovation and Research (MAScIR), Rabat Design Center, Rue Mohamed Al Jazouli, Madinat Al Irfane, 10100 Rabat, Morocco.
* e-mail: e.abdellaouialaoui@umi.ac.ma
This paper uses machine learning to predict road accidents in Morocco, a country marked by high annual accident rates. Our model employs data such as weather, time of day, and road conditions, derived from historical accidents and environmental records. Findings suggest that such predictive modeling can enable traffic authorities to anticipate high-risk situations and enact pre-emptive safety measures, contributing to significant reductions in road accidents. This study provides a data-driven approach towards policy implementation for road safety, with insights applicable to global road safety initiatives.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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