Short-Term Passenger Demand Forecasting Using Univariate Time Series Theory

  • Ondrej Cyprich University of Žilina
  • Vladimír Konečný University of Žilina
  • Katarína Kiliánová University of Žilina
Keywords: passenger demand, demand modelling, short-term demand forecasting, suburb bus transport

Abstract

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.

Author Biographiesaaa replica rolex repwatches replica rolex watches for men replica iwc watch

Ondrej Cyprich, University of Žilina

Department of road and urban transport

Ph.D. (27.08.2012)

Vladimír Konečný, University of Žilina

Department of road and urban transport

associate professor

Katarína Kiliánová, University of Žilina

Department of road and urban transport

Ph.D. student

References

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

Published
2013-12-16
How to Cite
1.
Cyprich O, Konečný V, Kiliánová K. Short-Term Passenger Demand Forecasting Using Univariate Time Series Theory. Promet [Internet]. 2013Dec.16 [cited 2024May30];25(6):533-41. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/338
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