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2015 | OriginalPaper | Chapter

Traffic Flow Forecasting Algorithm Based on Combination of Adaptive Elementary Predictors

Authors : Anton Agafonov, Vladislav Myasnikov

Published in: Analysis of Images, Social Networks and Texts

Publisher: Springer International Publishing

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Abstract

In this paper the problem of traffic flow prediction in the transport network of a large city is considered. For fast calculation of predictions, partition of a transport graph into a certain number of subgraphs based on the territorial principle is proposed. Next, we use a dimension reduction method based on principal components analysis to describe the spatio-temporal distribution of traffic flow condition in subgraphs. A short-term (up to 1 h) traffic flow prediction in each subgraph is calculated by an adaptive linear combination of elementary predictions. In this paper, the elementary predictions are Box-Jenkins time-series models, support vector regression, and the method of potential functions. The proposed traffic prediction algorithm is implemented and tested against the actual travel times over a large road network in Samara, Russia.

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Metadata
Title
Traffic Flow Forecasting Algorithm Based on Combination of Adaptive Elementary Predictors
Authors
Anton Agafonov
Vladislav Myasnikov
Copyright Year
2015
Book
Analysis of Images, Social Networks and Texts

Print ISBN: 978-3-319-26122-5

Electronic ISBN: 978-3-319-26123-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26123-2_16

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