01024662

Component

This paper reports on real-data results for a real-time 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 macroscopic traffic flow modeling and extended Kalman filtering that are outlined in the paper. One major innovative feature of the estimator is the on-line estimation of important model parameters (free speed, critical density, and capacity) along 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 the basic properties of the estimator (e.g. the significance of the on-line model parameter estimation) and, second, to reveal some adaptive capabilities of the estimator that enable the other two advantages of the estimator. The achieved testing results are quite satisfactory and promising for subsequent work.

01020180

06-0239

English

29p

2006

Transportation Research Board 85th Annual Meeting

CD-ROM

Figures (5) ; References (7)

Calibration; Freeways; Highway capacity; Highway traffic; Kalman filtering; Light; Macroscopic traffic flow; Real time data processing; Traffic density; Traffic incidents; Weather

Estimators; Free speed; Online estimating; Parameters; Stochastic model; Traffic state estimation

Highways; Operations and Traffic Management; I71: Traffic Theory

Transportation Research Board Annual Meeting 2006 Paper #06-0239

TRIS, TRB

Mar 3 2006 10:18AM