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Junction Traffic Prediction, Using Adjacent Junction Traffic Data, Based on Neural Networks

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 844)

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.

Keywords

Neural network structure Traffic flow prediction Intelligent transport system 

Notes

Acknowledgments

The authors wish to thank ZIR-SSR Bytom for providing video detector data from the Traffic Control Centre Gliwice site.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of TransportSilesian University of TechnologyKatowicePoland

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