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

Seif-Eddine Attoui (1) , Maroua Meddeb (1)

1 IRT SystemX

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.

Mots clés: Intelligent transportation system; traffic forecast; time series forcast; short-term; data preprocessing; data balancing; smart cities; Intelligent Transportation Systems; Traffic forecasting; Short-term; Data preprocessing; Data balancing

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Machine Learning [stat.ML]; Infrastructures de transport

Seif-Eddine Attoui, Maroua Meddeb. A generic framework for forecasting short-term traffic conditions on urban highways. 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Oct 2021, Porto, Portugal. pp.1-10, ⟨10.1109/DSAA53316.2021.9564192⟩. ⟨hal-03511152⟩