Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET
IEEE Transactions on Intelligent Transportation Systems
An adaptive freeway traffic state estimator
Automatica (Journal of IFAC)
An approach to urban traffic state estimation by fusing multisource information
IEEE Transactions on Intelligent Transportation Systems
Lagrangian sensing: traffic estimation with mobile devices
ACC'09 Proceedings of the 2009 conference on American Control Conference
Particle filter for platoon based models of urban traffic
Proceedings of the 15th WSEAS international conference on Systems
Hi-index | 22.15 |
This paper formulates the problem of real-time estimation of traffic state in freeway networks by means of the particle filtering framework. A particle filter (PF) is developed based on a recently proposed speed-extended cell-transmission model of freeway traffic. The freeway is considered as a network of components representing different freeway stretches called segments. The evolution of the traffic in a segment is modelled as a dynamic stochastic system, influenced by states of neighbour segments. Measurements are received only at boundaries between some segments and averaged within possibly irregular time intervals. This limits the measurement update in the PF to only these time instants when a new measurement arrives, while in between measurement updates any simulation model can be used to describe the evolution of the particles. The PF performance is validated and evaluated using synthetic and real traffic data from a Belgian freeway. An unscented Kalman filter is also presented. A comparison of the PF with the unscented Kalman filter is performed with respect to accuracy and complexity.