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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Acknowledgements
This work was supported by the Russian Foundation for Basic Research (RFBR) grant №13-07-12103-ofi-m, grant №13-01-12080-ofi-m, grant №12-07-0021-a and by the Ministry of Education and Science of the Russian Federation in the framework of the implementation of the Program of increasing the competitiveness of SSAU among the world-leading scientific and educational centers for 2013–2020 years.
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Agafonov, A., Myasnikov, V. (2015). Traffic Flow Forecasting Algorithm Based on Combination of Adaptive Elementary Predictors. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_16
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DOI: https://doi.org/10.1007/978-3-319-26123-2_16
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