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In modern transportation systems, traffic congestion is inevitable. Tominimize the loss caused by congestion, various control strategies have beendeveloped most of which rely on observing real-time traffic conditions. Asvintage traffic sensors are limited, traffic density estimation is very helpfulfor gaining network-wide observability. This paper deals with this problem byfirst, presenting a traffic model for stretched highway having multiple rampsbuilt based on asymmetric cell transmission model (ACTM). Second, based on theassumption that the encompassed nonlinearity of the ACTM is Lipschitz, a robustdynamic observer framework for performing traffic density estimation isproposed. Numerical test results show that the observer yields a sufficientperformance in estimating traffic densities having noisy measurements, whilebeing computationally faster the Unscented Kalman Filter in performingreal-time estimation.

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