Method of creating a computerized model for computing values representative of traffic congestion in respect of a geographic area for use in representing a degree of traffic congestion in the geographic area using a limited number of symbols, comprising: retrieving, in respect of roads within geographic area, historical traffic data and values representative of traffic congestion; deriving a computerized model for computing values representative of traffic congestion in respect of roads within the geographic area based on the retrieved information. Method of representing a degree of traffic congestion in a geographic area using a limited number of symbols, comprising: receiving recent traffic and weather data including one of recent average vehicle speed and recent average vehicle transit time and two of temperature, relative humidity, barometric pressure, and cloud cover; and computing a value representative of traffic congestion using the recent traffic and weather data and a trained artificial neural network.
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The method by which the above noted representations of the degrees of traffic are derived uses what is known in the art as “an artificial neural network” or sometimes simply “a neural network”. A neural network is a mathematical model inspired by biological neural networks. In this present implementation, the neural networks used are the “Fast Artificial Neural Network”
United States Patent Application 20160124906
Inventors: Karpov, Victor Vladimirovich (Moscow, RU)
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