An adaptive freeway traffic state estimator


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

Similar works

Full text

oaioai:art.torvergata.it:2108/18275Last time updated on 3/29/2020View original full text link

This paper was published in ART.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.