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In this paper, we aim to illustrate different approaches we followed whiledeveloping a forecasting tool for highway traffic in Morocco. Two mainapproaches were adopted: Statistical Analysis as a step of data exploration anddata wrangling. Therefore, a beta model is carried out for a betterunderstanding of traffic behavior. Next, we moved to Machine Learning where weworked with a bunch of algorithms such as Random Forest, Artificial NeuralNetworks, Extra Trees, etc. yet, we were convinced that this field of study isstill considered under state of the art models, so, we were also covering anapplication of Long Short-Term Memory Neural Networks.

Machine Learning Approaches for Traffic Volume Forecasting: A Case Study of the Moroccan Highway Network