Investigation of the adaptive features of a real-time nonlinear freeway traffic state estimator

This paper reports on real-data testing results for a real-time nonlinear freeway traffic state estimator with particular focus on its adaptive features. The pursued general approach to the real-time adaptive estimation of the complete traffic state in freeway stretches is based on stochastic nonlinear macroscopic traffic flow modeling and extended Kalman filtering that are outlined in the paper. One major innovative aspect of the estimator is the on-line estimation of important model parameters (free speed, critical density, and capacity) simultaneously with the estimation of traffic flow variables (flows, mean speeds, and densities), which leads to three significant advantages of the traffic state estimator: (1) avoidance of off-line model calibration; (2) automatic adaptation to changing external conditions (e.g. weather and light conditions); (3) enabling of incident alarms. The purpose of the reported real-data testing is, first, to demonstrate advantage (1) by investigating some basic properties of the estimator and, second, to explore some adaptive capabilities of the estimator that enable the other two advantages. The achieved testing results are quite satisfactory and promising for subsequent work.