Machine Learning Approaches for Traffic Volume Forecasting: A Case Study of the Moroccan Highway Network
This is an incremental application of existing methods to a new dataset for traffic forecasting in Morocco.
The paper tackled traffic volume forecasting for the Moroccan highway network by applying statistical analysis and machine learning algorithms, including Random Forest, Artificial Neural Networks, and Long Short-Term Memory Neural Networks, but did not report specific numerical results.
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.