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2024 | OriginalPaper | Chapter

Road Accidents Forecasting: An Uncertainty Quantification Model for Pre-disaster Management in Moroccan Context

Authors : Hajar Raillani, Lamia Hammadi, Abdessamad El Ballouti, Vlad Stefan Barbu, Babacar Mbaye Ndiaye, Eduardo Souza de Cursi

Published in: Proceedings of the 6th International Symposium on Uncertainty Quantification and Stochastic Modelling

Publisher: Springer International Publishing

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Abstract

This chapter delves into the critical area of road accident forecasting in Morocco, employing advanced uncertainty quantification models to enhance pre-disaster management. By analyzing historical road accident data, the study identifies significant trends and seasonal patterns, revealing a high frequency of accidents and injuries, particularly during the summer months. The authors utilize the Collocation method to develop accurate cumulative distribution functions (CDFs) for accidents, deaths, and injuries, enabling the calculation of probabilities for different human impact classes. This approach offers valuable insights for decision-makers, aiding in the development of targeted interventions to improve road safety and mitigate the impact of accidents. The chapter also highlights the importance of considering uncertainty in disaster modeling, providing a robust framework for predicting and managing road accidents effectively.

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Title: Road Accidents Forecasting: An Uncertainty Quantification Model for Pre-disaster Management in Moroccan Context
Authors: Hajar Raillani
Lamia Hammadi
Abdessamad El Ballouti
Vlad Stefan Barbu
Babacar Mbaye Ndiaye
Eduardo Souza de Cursi
Publisher: Springer International Publishing
Book: Proceedings of the 6th International Symposium on Uncertainty Quantification and Stochastic Modelling
Print ISBN: 978-3-031-47035-6

Electronic ISBN: 978-3-031-47036-3

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-47036-3_20

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