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Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation
2015
IET Control Theory & Applications
This paper proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described in this paper using a mode-dependent first order autoregressive (AR) stochastic process. The parameters of the AR-process take
doi:10.1049/iet-cta.2014.0909
fatcat:vrsoaspr2bhb5pzpfd5og5gjjq