The purpose of the paper is to identify and analyse the forecasting performance of the model of passenger demand for suburban bus transport time series, which satisfies the statistical significance of its parameters and randomness of its residuals. Box-Jenkins, exponential smoothing and multiple linear regression models are used in order to design a more accurate and reliable model compared the ones used nowadays. Forecasting accuracy of the models is evaluated by comparative analysis of the calculated mean absolute percent errors of different approaches to forecasting. In accordance with the main goal of the paper was identified the ARIMA model, which fulfils almost all statistical criterions with an exception of the model residuals normality. In spite of the limitation, the best forecasting abilities of identified model have been proven in comparison with other approaches to forecasting in the paper. The published findings of research will have positive influence on increasing the forecasting accuracy in the process of passenger demand forecasting.
Gnap, J., Poliak, M., Konečný, V.: 2008a. Prognóza vývoja pre okresy Žilinského kraja obsluhované SAD Žilina. Žilina: FPEDaS ŽU v Žiline; 2008
Gnap, J., Poliak, M., Konečný, V.: 2008b. Prognóza vývoja pre okresy Žilinského kraja obsluhované SAD Liptovský Mikuláš. Žilina: FPEDaS ŽU v Žiline; 2008
Cyprich, O.: Modelovanie dopytu cestujúcich po prímestskej autobusovej doprave. Žilina: Žilinská univerzita v Žiline, Fakulta prevádzky a ekonomiky dopravy a spojov, Katedra cestnej a mestskej dopravy; 2012
Jugović, A., Hess, S., Jugovic, T.P.: Traffic demand forecasting for port services. Promet - Traffic&Transportation [Internet]. 2011 [cited 2012 April 14]; 23(1): 59-69. Available from: http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/149/56
Brnjac, N., Abramović, B., Maslarić, M.: Forecasting intermodal transport requirements on corridor X. Promet - Traffic&Transportation [Internet]. 2010 [cited 2012 April 20]; 22(4): 303-307. Available from: http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/195/100
Krasić, D., Gatti, P.: Forecasting methodology of maritime passenger demand in a tourist destination. Promet - Traffic&Transportation [Internet]. 2009 [cited 2012 April 20]; 21(3): 183-190. Available from: http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/224/129
Dicová, J., Ondruš, J.: Trend of public mass transport indicators – as a tool of transport management and development of regions: Communications – Scientific Letters of the University of Žilina. 2010; 12(3A): 121-126
Karlaftiss, M.G., Vlahogianni, E.I.: Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C [Internet]. 2011 [cited 2012 April 13; 19 (3): 387-399. Available from: http://www.sciencedirect.com/science/article/pii/S0968090X10001610
Cyprich, O.: Application of Univariate Time Series Theory to Passenger Demand Forecasting: Communications – Scientific Letters of the University of Žilina. 2011
SAS LE 4.1 [software]. Cary, NC : SAS Institute Inc. 2006
SAS 9.1.3 [software]. Cary, NC : SAS Institute Inc. 2003
Cyprich, O.: Modelovanie vývoja vybraných kvantitatívnych ukazovateľov ako nástroja riadenia dopravnej spoločnosti, Ph.D. thesis concept. Žilina: University of Žilina; 2010
Cipra, T.: Analýza časových řad s aplikacemi v ekonomii. Praha/Bratislava: STNL/ALFA; 1986
Arlt, J., Arltová, M.: Ekonomické časové řady. Praha: Professional Publishing; 2009
Chatfield, Ch., Yar, M.: Holt-Winters forecasting: some practical issues: The Statistician; 1988
Dagum, E.B.: The X-11-ARIMA/88 Seasonal Adjustment Method: Foundations and User´s Manual, Statistics Canada. Ottawa; 1988
U.S. Bureau of the Census: X-12-ARIMA Seasonal Adjustment Program - Version 0.2.8, U.S. Bureau of the Census. Washington; 2001
U.S. Bureau of the Census: X-12-ARIMA Reference Manual - Version 0.2.8, U.S. Bureau of the Census. Washington; 2001
Leonard, M.: Large-Scale Automatic Forecasting. Millions of Forecasts [Internet]. 2002 [cited 2012 April 28]. Available from: https://support.sas.com/rnd/app/papers/largescale.pdf
Filiben, J.J., Heckert, A.: Exploratory data analysis. NIST/SEMATECH e-Handbook of Statistical Methods, NIST/SEMATECH, [Internet]. 2003 [cited 2012 April 15]; Available from: http://www.itl.nist.gov/div898/handbook/
Ljung, G.M., Box GEP. On the measure of lack fit in time series models: Biometrika. 1978
Hamilton, J.D.: Time Series Analysis. Princeton: Princeton University Press; 1994
Dickey, D.A., Hasza, D.P., Fuller, W.A.: Testing for unit roots in seasonal time series: Journal of the American Statistical Association. 1984
Shapiro, S.S., Wilk, M.B.: An analysis of variance test for normality (complete samples): Biometrika. 1965
Marček, D., Marček, M.: Analýza, modelovanie a prognózovanie časových radov s aplikáciami v ekonomike. Žilina: EDIS; 2001
Duke University: What to look for in regression model output [online], Duke university, Durham, [cited 2012 April 16] available from: http://www.duke.edu/~rnau/411regou.htm
Cyprich, O., Holeša, L.: Analýza použiteľnosti metódy X-12-ARIMA pri prognózovaní a dekompozícii časových radov dopytu cestujúcich: Perners Contacts, 2012; 7(1): 13-25
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