Road freight transport often requires the prediction of volume. Such knowledge is necessary to capture trends in the industry and support decision making by large and small trucking companies. The aim of the presented work is to demonstrate that application of some artificial intelligence methods can improve the accuracy of the forecasts. The first method employed was double exponential smoothing. The modification of this method has been proposed. Not only the parameters but also the initial values were set in order to minimize the mean absolute percentage error (MAPE) using the artificial immune system. This change resulted in a marked improvement in the effects of minimization, and suggests that the variability of the initial value of S2 has an impact on this result. Then, the forecasting Bayesian networks method was applied. The Bayesian network approach is able to take into account not only the historical data concerning the volume of freight, but also the data related to the overall state of the national economy. This significantly improves the quality of forecasting. The application of this approach can also help in predicting the trend changes caused by overall state of economy, which is rather impossible when analysing only the historical data.
Lawton R. How should additive Holt–Winters estimates be corrected? Int. J. Forecast. 1998;14(3):393-403. doi:10.1016/S0169-2070(98)00040-5
Pinto R, Gaiardelli P. Setting forecasting model parameters using unconstrained direct search methods: An empirical evaluation. Expert Syst. Appl. 2013;40(13):5331-5340. doi:10.1016/j.eswa.2013.03.044
Yager RR. Exponential smoothing with credibility weighted observations. Information Sciences. 2013;252:96-105. doi:10.1016/j.ins.2013.07.008
Lin K-P, Pai P-F, Yang S-L. Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms. Appl. Math. Comput. 2011;217(12):5318-5327. doi:10.1016/j.amc.2010.11.055
Lénárt B. Automatic identification of ARIMA models with neural network. Period. Polytech. Transp. 2011;39(1):39-42. doi:10.3311/pp.tr.2011-1.07
Eisuke K, Maasaki H, Takao M. Application of Bayesian Network to stock price prediction. Artif. Intell. Research. 2012;1(2):171-184.
Acar Y, Gardner ES. Forecasting method selection in a global supply chain. Int. J. Forecast. 2012;28(4):842–848. doi:10.1016/j.ijforecast.2011.11.003
A guidebook for forecasting freight transportation demand. Cambridge Systematics, Inc. 1997; NCHRPreport 388, Transportation Research Board, National Research Council, Washington, D.C.
Wierzchoń ST. Sztuczne systemy immunologiczne. Teoria i zastosowania. Warszawa Akademicka Oficyna Wydawnicza EXIT; 2001. Polish.
Bolstad WM. Introduction to Bayesian statistics. Wiley-Interscience; 2004.
Skrobisz C. Bayesian Prediction for Non-Full Information on the Example of Electricity, Folia Pomer. Univ. Technol. Stetin., Oeconomica. 2010;280(59):99-108.
Cheng J, Druzdzel MJ. An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. J. Artif. Intell. 2000;13:155-188.
Heckerman D, Geiger D, Chickering DM. Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 1995;3:197-243.
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