Abstract
Traffic congestions have a strong impact on the life of modern cities. Reducing congestion is one of the main concerns of private, urban and public institutions. Much effort has been devoted to the scientific research of this problem. One of the areas of these studies is the prediction of congestion. The forecast helps to distribute traffic on urban highways, thereby minimizing the congestion of individual sections and improve the road situation as a whole. There are special Internet services that analyze traffic congestion and provide users with information. They make a forecast of traffic congestions on the basis of statistics, however, as practice shows, these forecasts are not very accurate. Recently, neural network forecasting methods have been actively developing. In this paper, we investigate the possibility of recurrent neural networks with controlled synapses to predict traffic congestions. On the example of the famous Internet service “Yandex.Probki” is shown that the neural network is able to give more accurate predictions.
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Osipov, V., Miloserdov, D. (2019). Neural Network Forecasting of Traffic Congestion. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O., Musabirov, I. (eds) Digital Transformation and Global Society. DTGS 2019. Communications in Computer and Information Science, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-37858-5_20
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DOI: https://doi.org/10.1007/978-3-030-37858-5_20
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