Claudio L. Marte, Leopoldo R. Yoshioka, Caio F. Fontana, José Mauro Marquez
Estimation of highway traffic service level based on Module of Intelligent Transportation System and Artificial Intelligence
International experience shows that several organizations have used traffic data and events occurred on roadways to build quality indicators. This paper explores methods that allow estimating traffic conditions at a given moment or over a given period of time. Traffic data were collected using Module of Intelligent Transport System (MITS), and were processed by means of Artificial Intelligence (AI) techniques based on Highway Capacity Manual (HCM) methodology. Part of these data were used to train the Artificial Neural Network (ANN) and another part were used to compare with that estimated by ANN. Experimental results show that ANN has estimated the level of service (LOS) of traffic with less than 3% error. This method can help the highway users decide the best window time for your displacement.
Intelligent Transport Systems, Highway Capacity Manual, Artificial Neural Networks, Artificial Intelligence, ITS, HCM, ANN.
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Cite this paper
Claudio L. Marte, Leopoldo R. Yoshioka, Caio F. Fontana, José Mauro Marquez. (2016) Estimation of highway traffic service level based on Module of Intelligent Transportation System and Artificial Intelligence. International Journal of Computers, 1, 33-41