Combining a Gauss-Markov model and Gaussian process for traffic prediction in Dublin city center
نویسندگان
چکیده
We consider a city where induction-based vehicle count sensors are installed at some, but not all street junctions. Each sensor regularly outputs a count and a saturation value. We first use a discrete time Gauss-Markov model based on historical data to predict the evolution of these saturation values, and then a Gaussian Process derived from the street graph to extend these predictions to all junctions. We construct this model based on real data collected in Dublin city.
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