Real Data Testing of Model Parameter Estimation Methods for Macroscopic Traffic Flow Model

Abstract

In this paper a comparison for non-adaptive and adaptive traffic state estimators based on the nonlinear macroscopic traffic flow model is presented. In non-adaptive estimator, a Least Square based method was used to estimate model parameters with off-line data. Beside this non-adaptive estimator, three adaptive estimators were designed and tested. In these adaptive estimators, online estimation of the model parameters were done based on Least Square, joint filtering and dual filtering methods. In all mentioned estimators, extended Kalman filtering method was used to estimate traffic variables. Finally, Real data testing results of these estimators for Interstate 494 in metro freeway, Minnesota, USA are presented.