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arXiv · 2002.03530

Asymmetric Cell Transmission Model-Based, Ramp-Connected Robust Traffic Density Estimation under Bounded Disturbances

Suyash C. Vishnoi·Sebastian A. Nugroho·Ahmad F. Taha·Christian Claudel·Taposh Banerjee
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Abstract

In modern transportation systems, traffic congestion is inevitable. To minimize the loss caused by congestion, various control strategies have been developed most of which rely on observing real-time traffic conditions. As vintage traffic sensors are limited, traffic density estimation is very helpful for gaining network-wide observability. This paper deals with this problem by first, presenting a traffic model for stretched highway having multiple ramps built based on asymmetric cell transmission model (ACTM). Second, based on the assumption that the encompassed nonlinearity of the ACTM is Lipschitz, a robust dynamic observer framework for performing traffic density estimation is proposed. Numerical test results show that the observer yields a sufficient performance in estimating traffic densities having noisy measurements, while being computationally faster the Unscented Kalman Filter in performing real-time estimation.

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BibTeXRIS

Suyash C. Vishnoi, Sebastian A. Nugroho, Ahmad F. Taha, Christian Claudel, Taposh Banerjee. 2020-02-10. Asymmetric Cell Transmission Model-Based, Ramp-Connected Robust Traffic Density Estimation under Bounded Disturbances. https://arxiv.org/abs/2002.03530

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