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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References
Chrobok R, Kaumann O, Wahle J, Schreckenberg M (2004) Different methods of traffic forecast based on real data. Eur J Oper Res 155(3):558–568
Chen H, Grant-Muller S, Mussone L, Montgomery F (2001) A study of hybrid neural network approaches and the effects of missing data on traffic forecasting. Neural Comput Appl 10:277–286
Tan MC, Wong SC, Xu JM, Guan ZR, Zhang P (2009) An aggregation approach to short-term traffic flow prediction. IEEE Trans Intell Trans Syst 10:60–69
Vlahogianni EI, Karlaftis MG, Golias JC (2005) Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp Res Part C 13:211–234
Pamuła T (2012) Traffic flow analysis based on the real data using neural networks. In: Mikulski J (ed) Telematics in the transport environment. Selected papers. Springer, Berlin, pp 364–371
Bolstad WM (2004) Introduction to Bayesian statistics. Wiley-Interscience, Hoboken
Srinivasan D, Choy MC, Cheu RL (2006) Neural networks for real-time traffic signal control. IEEE Trans Intell Trans Syst 7(3):261–271
Skrobisz C (2010) Bayesian prediction for non-full information on the example of electricity. Folia Pomer Univ Technol Stetin Oeconomica 280(59):99–108
Pamuła T (2012) Classification and prediction of traffic flow based on real data using neural networks, Arch Transp 24(4):519–530
GeNie package (2014). http://genie.sis.pitt.edu/
Cheng J, Druzdzel MJ (2000) An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. J Artif Intell Res 13:155–188
Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20(3):197–243
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B 39(1):1–38
West M, Harrison J (1997) Bayesian forecasting and dynamic models, 2nd edn. Springer, New York, p 34
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Pamuła, T., Król, A. (2016). The Traffic Flow Prediction Using Bayesian and Neural Networks. In: Sładkowski, A., Pamuła, W. (eds) Intelligent Transportation Systems – Problems and Perspectives. Studies in Systems, Decision and Control, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-319-19150-8_4
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DOI: https://doi.org/10.1007/978-3-319-19150-8_4
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