A supervised switching-mode observer of traffic state and parameters and application to adaptive ramp metering

Abstract

Traffic state observers derived from the cell transmission model (CTM) are vulnerable to incorrect information and time variation of traffic flow parameters, in particular the critical density, known as the issue of mismodelling, which can cause erroneous mode switching of CTM-based observers to undermine estimation and control. This issue cannot be completely overcome, even if traffic flow parameters are augmented into state vector. We propose coupling a supervisor to the standard CTM-EKF observer to completely resolve this issue. Simulation shows that the proposed supervised observer can switch modes in accordance with actual situations and generate quality estimates of both traffic state and parameters. The supervised observer is then integrated with ramp metering control. Simulation shows that, in an environment of time-varying traffic flow parameters, the supervised observer-based ramp metering control system considerably outperforms an ordinary observer-based ramp metering control system, which only updates traffic state in real time.

Keywords:

• Traffic state estimation
• parameter estimation
• extended Kalman filter
• CUSUM
• cell transmission model
• ramp metering

Acknowledgments

This work was supported in part by the NSF CIS Award #1949710. Any Opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the National Science Foundation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the NSF CIS Award #1949710, and in part by the C2SMART University Transportation Center under USDOT Award No. 69A3551747124.