Dual EKF State and Parameter Estimation in Multi-Class First-Order Traffic Flow Models

Abstract

In this paper we investigate a real-time traffic surveillance system based on a multi class first-order traffic flow model called Fastlane. We demonstrate a dual extended Kalman Filtering approach in which the model state and parameters can be estimated simultaneously from real-time data. The innovation is that although Fastlane maintains the dynamics of multiple vehicular classes (e.g. trucks, buses, cars), only the total mixed-class density is corrected by the filter, which is ‘translated’ into multi-class state corrections by means of state-dependent person car equivalents and class flow shares. Results on real data from a densely used freeway show that the DEKF procedure is able to reproduce accurate speeds and flows and physically plausible parameters.