Real-time parallel parameter estimators for a second-order macroscopic traffic flow model

The online estimation of traffic flow characteristics could be used for traffic control, incident management etc. This paper presents a real-time parameter estimation scheme based on a second-order macroscopic traffic flow model. The online estimation of the key parameters does not follow from standard estimation techniques due to the fact that the unknown parameters cannot be expressed in the form of a linear parametric model. In this paper we bypass this problem by using parallel estimators and an appropriate logic to choose the one that generates more accurate estimates. One month field traffic data from the Berkeley Highway Laboratory (BHL) are used to demonstrate the effectiveness of the proposed approach