This paper proposes a method for estimating traffic flows on some links of a road network\nknowing the data on other links that are monitored with sensors. In this way, it is possible to\nobtain more information on traffic conditions without increasing the number of monitored links.\nThe proposed method is based on artificial neural networks (ANNs), wherein the input data are\nthe traffic flows on some monitored road links and the output data are the traffic flows on some\nunmonitored links. We have implemented and tested several single-layer feed-forward ANNs that\ndiffer in the number of neurons and the method of generating datasets for training. The proposed\nANNs were trained with a supervised learning approach where input and output example datasets\nwere generated through traffic simulation techniques. The proposed method was tested on a\nreal-scale network and gave very good results if the travel demand patterns were known and used\nfor generating example datasets, and promising results if the demand patterns were not considered\nin the procedure. Numerical results have underlined that the ANNs with few neurons were more\neffective than the ones with many neurons in this specific problem.
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