Particle filter-based real-time estimation and prediction of traffic conditions. In: Christos H. Skiadas (Ed.), Recent Advances in Stochastic Modelling and Data Analysis
SAU ; EL-FAOUZI ; BEN-AISSA ; DE MOUZON
Type de document
CHAPITRE D'OUVRAGE (CO)
Langue
anglais
Auteur
SAU ; EL-FAOUZI ; BEN-AISSA ; DE MOUZON
Résumé / Abstract
Real-time estimation and short-term prediction of traffic conditions is one of major concern of traffic managers and ITS-oriented systems. Model-based methods appear now as very promising ways in order to reach this purpose. Such methods are already used in process control (Kalman filtering, Luenberger observers). In the application presented in this paper, due to the high non linearity of the traffic models, particle filter (PF) approach is applied in combination with the well-known first order macroscopic traffic model. Not only shall we show that travel time prediction is successfully realized, but also that we are able to estimate, in real time, the motorway traffic conditions, even on points with no measurement facilities, having, in a way, designed a virtual sensor. Real-time traffic estimation, Bayesian Monte Carlo, travel time prediction
Editeur
World Scientific Publishing
