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

TL;DR

This paper compares statistical and machine learning methods for forecasting highway traffic in Morocco, demonstrating the effectiveness of various algorithms including neural networks and LSTM models.

Contribution

It introduces a comprehensive approach combining statistical analysis and multiple machine learning algorithms for traffic forecasting in Morocco.

Findings

LSTM models showed promising results in traffic prediction.

Machine learning algorithms outperformed traditional statistical methods.

The study provides a benchmark for traffic forecasting models in Moroccan highways.

Abstract

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