Simultaneous traffic count and ramp flow estimation for multilane freeways based on Markov models

As a distributed parameter system, traffic flow model of freeway traffic is determined by the traffic state on-road and boundary flows form on-ramp or off-ramp sections. The existing studies for traffic estimation mainly focus on the traffic parameter, namely density (or vehicles) of mainline traffic. In this paper, Kalman filtering for simultaneous traffic counts and off-ramp flow estimation is proposed with the linearization of the speed density observation equation. The state-space model is formulated by using a Markov chain to describe the vehicles' lane-change movements. Numerical studies are carried out to investigate the performance of the developed approach.