Estimation of highway traffic from sparse sensors: Stochastic modeling and particle filtering

Traffic control is essential for the achievement of a sustainable and safe mobility. Monitoring systems deployed over the roads collect a great amount of traffic data that must be efficiently processed by statistical methods to draw traffic macroparameters that are needed for control operations. In this paper we propose a particle filtering approach to estimate the density over a road network starting from noisy and sparse measurements provided by road-embedded sensors. We propose a new Bayesian framework based on the link-node cell transmission model to take into account the stochastic behavior of traffic and the hysteresis phenomenon that are typically observed in real data. Numerical tests show that the estimation method is able to reliably reconstruct the traffic field even in case of very sparse sensor deployments.