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

Abderrahim Khalifa; Younes Idsouguou; Loubna Benabbou; Mourad Zirari

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

Computers and Society 2017-11-21 v1 Machine Learning

Authors: Abderrahim Khalifa , Younes Idsouguou , Loubna Benabbou , Mourad Zirari

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.

Keywords

traffic volume forecasting machine learning highway traffic statistical analysis beta model random forest long short-term memory

Cite

@article{arxiv.1711.06779,
  title  = {Machine Learning Approaches for Traffic Volume Forecasting: A Case Study of the Moroccan Highway Network},
  author = {Abderrahim Khalifa and Younes Idsouguou and Loubna Benabbou and Mourad Zirari},
  journal= {arXiv preprint arXiv:1711.06779},
  year   = {2017}
}

Comments

Presented at NIPS 2017 Workshop on Machine Learning for the Developing World

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

Abstract

Keywords

Cite

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