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Autonomic Transport Management Systems—Enabler for Smart Cities, Personalized Medicine, Participation and Industry Grid/Industry 4.0 Read chapter Read first chapter

2016 | OriginalPaper | Chapter

The Traffic Flow Prediction Using Bayesian and Neural Networks

Authors : Teresa Pamuła, Aleksander Król

Published in: Intelligent Transportation Systems – Problems and Perspectives

Publisher: Springer International Publishing

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Abstract

The article presents two short-term forecasting models for determining the traffic flow volumes. The road traffic characteristics are essential for identification the trends in the distribution of the road traffic in the network, determination the capacity of the roads and the traffic variability over the time. The presented model is based on the historical, detailed data concerning the road traffic. The aim of the study was to compare the short-term forecasting models based on Bayesian networks (BN) and artificial neural networks (NN), which can be used in traffic control systems especially incorporated into modules of Intelligent Transportation Systems (ITS). Additionally the comparison with forecasts provided by the Bayesian Dynamic Linear Model (DLM) was performed. The results of the research shows that artificial intelligence methods can be successfully used in traffic management systems.

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previous chapter ITS Services Packages as a Tool for Managing Traffic Congestion in Cities
next chapter Dimensioning of Multiple Capacity Transport Line with Mutual Traffic Correlation
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Metadata
Title
The Traffic Flow Prediction Using Bayesian and Neural Networks
Authors
Teresa Pamuła
Aleksander Król
Copyright Year
2016
Book
Intelligent Transportation Systems – Problems and Perspectives

Print ISBN: 978-3-319-19149-2

Electronic ISBN: 978-3-319-19150-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-19150-8_4

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