A real-time freeway network traffic surveillance tool

作者：Wang YB^*; Papageorgiou M; Messmer A

来源：IEEE Transactions on Control Systems Technology, 2006, 14(1): 18-32.

DOI：10.1109/TCST.2005.859636

• extended Kalman filter
• freeway networks
• on-line model parameter estimation
• queue tracking
• stochastic macroscopic freeway network traffic flow model
• traffic state estimation and prediction
• traffic surveillance
• travel time estimation and prediction

This paper presents a real-time freeway network traffic surveillance tool RENAISSANCE. Based on a stochastic macroscopic freeway network traffic flow model and the extended Kalman filter, RENAISSANCE is fed with a limited amount of real-time traffic measurements to enable a number of freeway network traffic surveillance tasks, including traffic state estimation and prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction (queue tracking), and incident alarm. The paper first introduces the stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which a complete dynamic system model for freeway network traffic is established, with a special attention to the handling of some important model parameters. The addressed traffic surveillance tasks are described along with the functional architecture of RENAISSANCE. A simulation test was conducted for the tool with respect to a hypothetical freeway network example, while the traffic state estimator of RENAISSANCE was also tested with real traffic measurement data collected from a Bavarian freeway.

• 出版日期2006-1