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

Uncertainty quantification has become a major interest for researchers nowadays, particularly in the field of risk analysis and optimization under uncertainties. Uncertainty is an essential parameter to take into consideration in time series forecasting. In this field we aim to develop mathematical models based on uncertainty quantification tools for road accidents forecasting as a part of the pre-disaster management phase and also provide an anticipative visualization of the most sensitive zones to accidents in Morocco. To achieve this goal, we use the Interpolation-based approximation method for resolution in order to describe and analyze the road traffic accidents by defining the cumulative distribution functions (CDFs) of road accidental deaths and injuries. The obtained CDFs show that the distribution of road accidental deaths and injuries in Morocco varies according to seasons i.e., High season and Low season. These models can be used for making predictions of the future occurrence and human impact of road traffic accidents as a part of the pre-disaster management phase which complete and validate our disaster risk management approach as a decision-making tool dedicated to governments and humanitarian organizations. This work deals with humanitarian logistical field and aims to use the developed models for probabilistic calculation of the road traffic accidents behavior which helps in the preparation of the logistical fabric for the future events.