Real-data testing results of a real-time nonlinear freeway traffic state estimator are presented with a particular focus on its adaptive features. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic nonlinear macroscopic traffic flow modeling and extended Kalman filtering. One major innovative aspect of the estimator is the real-time joint estimation of traffic flow variables (flows, mean speeds, and densities) and some important model parameters (free speed, critical density, and capacity), which leads to four significant features of the traffic state estimator: (i) avoidance of prior model calibration; (ii) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (iii) enabling of incident alarms; (iv) enabling of detector fault alarms. The purpose of the reported real-data testing is, first, to demonstrate feature (i) by investigating some basic properties of the estimator and, second, to explore some adaptive capabilities of the estimator that enable features (ii)-(iv). The achieved testing results are quite satisfactory and promising for further work and field applications. (C) 2008 Elsevier Ltd. All rights reserved.

Wang, Y., Papageorgiou, M., Messmer, A., Coppola, P., Tzimitsi, A., Nuzzolo, A. (2009). An adaptive freeway traffic state estimator. AUTOMATICA, 45(1), 10-24 [10.1016/j.automatica.2008.05.019].

An adaptive freeway traffic state estimator

COPPOLA, PIERLUIGI;NUZZOLO, AGOSTINO
2009-01-01

Abstract

Real-data testing results of a real-time nonlinear freeway traffic state estimator are presented with a particular focus on its adaptive features. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic nonlinear macroscopic traffic flow modeling and extended Kalman filtering. One major innovative aspect of the estimator is the real-time joint estimation of traffic flow variables (flows, mean speeds, and densities) and some important model parameters (free speed, critical density, and capacity), which leads to four significant features of the traffic state estimator: (i) avoidance of prior model calibration; (ii) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (iii) enabling of incident alarms; (iv) enabling of detector fault alarms. The purpose of the reported real-data testing is, first, to demonstrate feature (i) by investigating some basic properties of the estimator and, second, to explore some adaptive capabilities of the estimator that enable features (ii)-(iv). The achieved testing results are quite satisfactory and promising for further work and field applications. (C) 2008 Elsevier Ltd. All rights reserved.
Pubblicato
Rilevanza internazionale
Articolo
Sì, ma tipo non specificato
Settore ICAR/05 - Trasporti
English
Con Impact Factor ISI
Stochastic macroscopic traffic flow model extended kalman filter freeway traffic state estimation joint state and parameter estimation congestion weather conditions traffic incidents detector faults traffic incident alarm detector fault alarm
Wang, Y., Papageorgiou, M., Messmer, A., Coppola, P., Tzimitsi, A., Nuzzolo, A. (2009). An adaptive freeway traffic state estimator. AUTOMATICA, 45(1), 10-24 [10.1016/j.automatica.2008.05.019].
Wang, Y; Papageorgiou, M; Messmer, A; Coppola, P; Tzimitsi, A; Nuzzolo, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/23602
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