Neural networks for local monitoring of traffic magnetic sensors

Nadhir Messai (1) , Philippe Thomas (2) , Dimitri Lefebvre (3) , Abdellah Elmoudni (4)

1 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804
2 CRAN - Centre de Recherche en Automatique de Nancy
3 GREAH - Groupe de Recherche en Electrotechnique et Automatique du Havre
4 SET - Laboratoire Systèmes et Transports

A real-time traffic incidents detection algorithm is proposed and applied to the monitoring of a complex road junction in the city of Nancy in France. This algorithm has the potential to provide local monitoring of traffic sensors. Our approach is based on macroscopic traffic flow models, and more precisely on the flow-density relationship. Once this relation is extracted from real traffic data, an admissible region is defined in the flow-density space. Then, the classification properties of neural networks are used to design the monitoring network, which detects and isolates the incidents that disturb the traffic when the measured data are out of the defined region

Mots clés: traffic modeling; traffic monitoring; STRADA; feed-forward neural networks; classification

Domaines: Automatique / Robotique

Nadhir Messai, Philippe Thomas, Dimitri Lefebvre, Abdellah Elmoudni. Neural networks for local monitoring of traffic magnetic sensors. Control Engineering Practice, 2005, 13 (1), pp.67-80. ⟨10.1016/j.conengprac.2004.02.005⟩. ⟨hal-00137707⟩