Online Set-Point Estimation for Feedback-Based Traffic Control Applications

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2023-10

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Department of Built Environment

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Language

en

Pages

13

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IEEE Transactions on Intelligent Transportation Systems, Volume 24, issue 10, pp. 10830-10842

Abstract

This paper deals with traffic control at motorway bottlenecks assuming the existence of an unknown, time-varying, Fundamental Diagram (FD). The FD may change over time due to different traffic compositions, e.g., light and heavy vehicles, as well as in the presence of connected and automated vehicles equipped with different technologies at varying penetration rates, leading to inconstant and uncertain driving characteristics. A novel methodology, based on Model Reference Adaptive Control, is proposed to robustly estimate in real-time the time-varying set-points that maximise the bottleneck throughput, particularly useful when the traffic is regulated via a feedback-based controller. Furthermore, we demonstrate the global asymptotic stability of the proposed controller through a novel Lyapunov analysis. The effectiveness of the proposed approach is evaluated via simulation experiments, where the estimator is integrated into a feedback ramp-metering control strategy, employing a second-order multi-lane macroscopic traffic flow model, modified to account for time-varying FDs.

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| openaire: EC/H2020/856602/EU//FINEST TWINS Publisher Copyright: Author

Keywords

Adaptation models, adaptive control, Adaptive control, Asymptotic stability, Delays, Estimation, Numerical models, robust estimation, time-varying fundamental diagram, Traffic control

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DOI

10.1109/TITS.2023.3274233

Citation

Tajdari, F & Roncoli, C 2023, 'Online Set-Point Estimation for Feedback-Based Traffic Control Applications', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 10, pp. 10830-10842. https://doi.org/10.1109/TITS.2023.3274233

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https://urn.fi/URN:NBN:fi:aalto-202306073672

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[article-cris] Insinööritieteiden korkeakoulu / ENG