A generic framework for forecasting short-term traffic conditions on urban highways

With the emergence of Connected and Smart Cities, the need to predict traffic conditions has led to the development of a large variety of forecasting algorithms. In spite of various research efforts, the choice of models and techniques strongly depends on the use case, the highway infrastructure as well as the provided dataset. This study is launched as part of a project which aims to design an Intelligent Transport System (ITS) dedicated to highway supervisors to regulate traffic. This system needs to be supplied by continuous, real-time forecasting of short-term traffic congestions in order to make decisions accordingly. In this paper, we propose a general framework that, first, performs different data preprocessing techniques to improve data quality, and second, provides real-time multiple horizons predictions. Our framework uses different models combining Machine learning and Deep learning algorithms. Experiments results confirmed the necessity of the data preprocessing step, especially with highly dynamic data and heterogeneous mobility contexts. In addition, our methodology is tested in a real case study and shows very encouraging results.