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2019 | OriginalPaper | Buchkapitel

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

verfasst von : Teresa Pamuła, Wiesław Pamuła

Erschienen in: Integration as Solution for Advanced Smart Urban Transport Systems

Verlag: Springer International Publishing

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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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Titel: Junction Traffic Prediction, Using Adjacent Junction Traffic Data, Based on Neural Networks
verfasst von: Teresa Pamuła
Wiesław Pamuła
Verlag: Springer International Publishing
Buch: Integration as Solution for Advanced Smart Urban Transport Systems
Print ISBN: 978-3-319-99476-5

Electronic ISBN: 978-3-319-99477-2

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-99477-2_5

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