论文信息 - Real Data Testing of Model Parameter Estimation Methods for Macroscopic Traffic Flow Model

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.

Karim Salahshoor | Ehsan Jafari | Behzad Moshiri | Amin Ramezani

[1] Petros A. Ioannou,et al. Real-time parallel parameter estimators for a second-order macroscopic traffic flow model , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[2] Markos Papageorgiou,et al. Parameter identification for a traffic flow model , 1979, Autom..

[3] Markos Papageorgiou,et al. Real-time freeway traffic state estimation based on extended Kalman filter: a general approach , 2005 .

[4] R. Horowitz,et al. Mixture Kalman filter based highway congestion mode and vehicle density estimator and its application , 2004, Proceedings of the 2004 American Control Conference.

[5] Markos Papageorgiou,et al. Modelling and real-time control of traffic flow on the southern part of Boulevard Peripherique in Paris: Part I: Modelling , 1990 .

[6] Markos Papageorgiou,et al. Motorway traffic surveillance and control , 2007, 2007 European Control Conference (ECC).

[7] Andreas Hegyi,et al. Freeway traffic estimation within particle filtering framework , 2007, Autom..

[8] Eric A. Wan,et al. Nonlinear estimation and modeling of noisy time series by dual kalman filtering methods , 2000 .

[9] Markos Papageorgiou,et al. Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing , 2008 .