Abstract
The paper discusses the problem of substitution of traffic data, used for prediction of traffic flows at a junction, with data from an adjacent junction. Such a case arises when the measuring resources at the junction malfunction. Neural networks based approach is used for forecasting traffic flows. Solutions incorporating a multilayer perceptron (MLP) network, a cascade forward network (CFN) and a deep learning network (DLN) with autoencoders are used for evaluating the prediction performance. The elaborated designs are validated using a data set of traffic flow measurements comprising over 15 thousand measurements collected in a period of over six months. Results prove that substituting data from an adjacent junction is justified for predicting traffic flows in case of malfunctioning measuring resources.
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The authors wish to thank ZIR-SSR Bytom for providing video detector data from the Traffic Control Centre Gliwice site.
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Pamuła, T., Pamuła, W. (2019). Junction Traffic Prediction, Using Adjacent Junction Traffic Data, Based on Neural Networks. In: Sierpiński, G. (eds) Integration as Solution for Advanced Smart Urban Transport Systems. TSTP 2018. Advances in Intelligent Systems and Computing, vol 844. Springer, Cham. https://doi.org/10.1007/978-3-319-99477-2_5
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DOI: https://doi.org/10.1007/978-3-319-99477-2_5
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