While going through a major rail transformation, it is important to develop reliable estimation models for rail passenger flows (RPFs) in Turkey. There are two main approaches in RPF estimation, regressions and autoregressive integrated moving-average (ARIMA) models, both of which were in this study developed using the daily RPF data for the period 2011–2015. The ARIMA models (with some variations) were used to forecast first the daily flows in 2016, during which travel restrictions for summer resulted in reduced volumes, successfully captured in the updated ARIMA model. The regression models predicted the expected demand during the restrictions, enabling evaluation of the impact of restrictions, which also showed the models’ power over the longer term. The forecasts were extended to 2017, 2018, and 2019 data. The regression results produced more reliable forecasts over the long term, whereas more accurate predictions were obtained by ARIMA-Sliding (FA-Sld) for short-term planning purposes.
LuoY. J.LiuJ.SunX.LaiQ. Y.Regression Model for Daily Passenger Volume of High-Speed Railway Line under Capacity Constraint. Journal of Central South University, Vol. 22, No. 9, 2015, pp. 3666–3676.
2.
XuX.DouY.ZhouZ.LiaoT.LuY.TanY.Railway Passenger Flow Forecasting Based on Time Series Analysis with Big Data. Proc., 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China, IEEE, New York, 2018, pp. 3584–3590.
3.
ÇuhadarM.Building Proper Forecast Model for Daily Air Passenger Demand: A Study of Antalya International Airport. Proc., International Antalya Hospitality Tourism and Travel Research Conference, Antalya, Türkiye, Akdeniz University, Tourism Faculty, December 9–12, 2014, pp. 467–476.
4.
PrakaulyaV.SharmaR.SinghU.ItareR.Railway Passenger Forecasting Using Time Series Decomposition Model. Proc., 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), Vol. 2, Coimbatore, India, IEEE, New York, April 20–22, 2017, pp. 554–558.
5.
SmithB.DemetskyM.Traffic Flow Forecasting: Comparison of Modeling Approaches. Journal of Transportation Engineering, Vol. 123, No. 4, 1997, pp. 261–266.
6.
StephanedesY. J.MichalopoulosP. G.PlumR. A.Improved Estimation of Traffic Flow for Real-Time Control (Discussion and Closure). Transportation Research Record: Journal of the Transportation Research Board, 1981. 795: 28–39.
7.
WilliamsB. M.DurvasulaP. K.BrownD. E.Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models. Transportation Research Record: Journal of the Transportation Research Board, 1998. 1644: 132–141.
8.
HansenJ. V.McDonaldJ. B.NelsonR. D.Time Series Prediction with Genetic-Algorithm Designed Neural Networks: An Empirical Comparison with Modern Statistical Models. Computational Intelligence, Vol. 15, No. 3, 1999, pp. 171–184.
9.
WilliamsB. M.Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling. Transportation Research Record: Journal of the Transportation Research Board, 2001. 1776: 194–200.
10.
LeeS.FambroD.Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting. Transportation Research Record: Journal of the Transportation Research Board, 1999. 1678: 179–188.
11.
KamarianakisY.PrastacosP.Space–Time Modeling of Traffic Flow. Computers & Geosciences, Vol. 31, No. 2, 2005, pp. 119–133.
12.
WangX.ZhangN.ChenY.ZhangY.Short-Term Forecasting of Urban Rail Transit Ridership Based on ARIMA and Wavelet Decomposition. In AIP Conference Proceedings, Vol. 1967, No. 1, May2018, AIP Publishing. https://doi.org/10.1063/1.5039099.
13.
Van Der VoortM.DoughertyM.WatsonS.Combining Kohonen Maps with ARIMA Time Series Models to Forecast Traffic Flow. Transportation Research Part C: Emerging Technologies, Vol. 4, No. 5, 1996, pp. 307–318.
14.
WeiY.ChenM.-C.Forecasting the Short-Term Metro Passenger Flow with Empirical Mode Decomposition and Neural Networks. Transportation Research Part C: Emerging Technologies, Vol. 21, No. 1, 2012, pp. 148–162.
15.
BoxG. E. P.JenkinsG. M. Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco, CA, 1970.
16.
PostorinoM. N.RussoF.Time Series Uni-mode or Random Utility Multimode Approach in National Passenger Models: The Impact on the Italian Air Demand Forecast. Proc., European Transport Conference (PTRC), Cambridge, 2001.
17.
LaiS. L.LuW. L.Impact Analysis of September 11 on Air Travel Demand in the USA. Journal of Airport Transport Management, Vol. 11, No. 6, 2005, pp. 455–458.
18.
LeeS.OhC.O’ LearyJ. T.Estimating the Impact of the September 11 Terrorist Attacks on the US Air Transport Passenger Demand Using Intervention Analysis. Tourism Analysis, Vol. 9, No. 4, 2005, pp. 355–361.
19.
AndreoniA.PostorinoM. N.A Multivariate ARIMA Model to Forecast Air Transport Demand. Proc., European Transport Conference (ETC), Strasbourg, France, Association for European Transport, London, 2006.
20.
PitfieldD. E.The Southwest Effect: A Time-Series Analysis on Passengers Carried by Selected Routes and a Market Share Comparison. Journal of Air Transport Management, Vol. 14, No. 3, 2008, pp. 113–122.
21.
SamagaioA.WoltersM.Comparative Analysis of Government Forecasts for the Lisbon Airport. Journal of Air Transport Management, Vol. 16, 2010, pp. 213–217.
22.
MinJ. C. H.KungH. K.LiuH. S.Interventions Affecting Air Transport Passenger Demand in Taiwan. African Journal of Business Management, Vol. 4, No. 10, 2010, pp. 2121–2131.
23.
XieG.WangS.LaiK. K.Short-Term Forecasting of Air Passenger by Using Hybrid Seasonal Decomposition and Least Squares Support Vector Regression Approaches. Journal of Air Transport Management, Vol. 37, 2014, pp. 20–26.
24.
AhmaedM. S.CookA. R.Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Technique. Transportation Research Record: Journal of the Transportation Research Board, 1979. 722: l–9.
25.
WoroniukC.MarinovM.ZunderT.MortimerP.Time Series Analysis of Rail Freight Services by the Private Sector in Europe. Transport Policy, Vol. 25, 2013, pp. 81–93.
26.
MakridakisS.SpiliotisE.AssimakopoulosV.Statistical and Machine Learning Forecasting Methods: Concerns and Ways Forward. PLoS One, Vol. 13, No. 3, 2018, p. e0194889.
27.
MakridakisS.AndersenA.CarboneR.The Accuracy of Extrapolation (Time Series) Methods: Results of a Forecasting Competition. Journal of Forecasting, Vol. 1, 1982, pp. 111–153.
28.
WickhamR. R.Evaluation of Forecasting Techniques for Short-Term Demand of Air Transportation. Massachusetts Institute of Technology, Department of Aeronautics & Astronautics, Cambridge, MA; Flight Transportation Lab, 1995.
29.
BoxG. E. P.JenkinsG. M.ReinselG. C.LjungG. M.Time Series Analysis: Forecasting and Control, 5th ed.John Wiley & Sons. Inc., Hoboken, 2016.
30.
HyndmanR. J.AthanasopoulosG.Forecasting: Principles and Practice, 2nd ed.OTexts, Melbourne, Australia, 2018.
31.
SuhartonoS.Time Series Forecasting by Using Seasonal Autoregressive Integrated Moving Average: Subset, Multiplicative or Additive Model. Journal of Mathematics and Statistics, Vol. 7, No. 1, 2011, pp. 20–27.
32.
PristaN.DiawaraN.CostaM. J.JonesC.Use of SARIMA Models to Assess Data-Poor Fisheries: A Case Study with a Sciaenid Fishery off Portugal. Fishery Bulletin, Vol. 109, No. 2, 2011, pp. 170–185.
33.
MilenkovićM.ŠvadlenkaL.MelicharV.BojovićN.AvramovićZ.SARIMA Modelling Approach for Railway Passenger Flow Forecasting. Transport, Vol. 33, No. 5, 2018, pp. 1113–1120. https://doi.org/10.3846/16484142.2016.1139623.
34.
KeilA.BernatE. M.CohenM. X.DingM.FabianiM.GrattonG.KappenmanE. S., et al. Recommendations and Publication Guidelines for Studies Using Frequency Domain and Time-frequency Domain Analyses of Neural Time Series. Psychophysiology, Vol. 59, No. 5, 2022, p. e14052.
35.
GretherD. M.NerloveM.Some Properties of “Optimal” Seasonal Adjustment. Econometrica: Journal of the Econometric Society, Vol. 38, No. 5, 1970, pp. 682–703.
36.
BellW.Signal Extraction for Nonstationary Time Series. The Annals of Statistics, Vol. 12, No. 2, 1984, pp. 646–664.
37.
HoltC. C.Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages. ONR Research Memorandum 52. Carnagie Institute of Technology, Pittsburgh, PA, 1957.
38.
KarapanagiotidisP.Literature Review of Modern Time Series Forecasting Methods. International Journal of Forecasting, Vol. 27, No. 4, 2012, pp. 11–79.
39.
PanG.LiS.ZhuY.A Time–Frequency Correlation Analysis Method of Time Series Decomposition Derived from Synchrosqueezed S Transform. Applied Sciences, Vol. 9, No. 4, 2019, p. 777.
40.
LiangM.Xi-HaiL.Wan-GangZ.Dai-ZhiL.The Generalized Cross-Correlation Method for Time Delay Estimation of Infrasound Signal. Proc., 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Qinhuangdao, China, IEEE, New York, September 2015, pp. 1320–1323.
41.
ArmstrongJ. S.Principles of Forecasting: A Handbook for Researchers and Practitioners. Kluwer Academic Publishers, Norwell, MA, 2001.
42.
VaragouliE.SimosT. E.XeidakisG.Fitting a Multiple Regression Line to Travel Demand Forecasting: The Case of the Prefecture of Xanthi, Northern Greece. Mathematical and Computer Modelling, Vol. 42, No. 7, 2005, pp. 817–836.
43.
AndersonM.SharfiK.GholstonS.Direct Demand Forecasting Model for Small Urban Communities Using Multiple Linear Regression. Transportation Research Record: Journal of the Transportation Research Board, 2006. 1981: 114–117.
44.
BlaineyS.MulleyC.Using Geographically Weighted Regression to Forecast Rail Demand in the Sydney Region. Proc., 36th Australasian Transport Research Forum Conference, Brisbane, Patrec, Western Australia, 2013, pp. 2–4.
45.
SivrikayaO.TunçE.Demand Forecasting for Domestic Air Transportation in Turkey. The Open Transportation Journal, Vol. 7, No. 1, 2013, pp. 20–26.
TortumA.GözcüO.ÇodurM. Y.Türkiye’de Hava Ulaşım Talebinin Arıma Modelleri ile Tahmin Edilmesi. Araştırma Makalesi Iğdır Üni. Fen Bilimleri Enst. Der., Vol. 4, No. 2, 2014, pp. 39–54.
JarqueC. M.Jarque-Bera Test. In International Encyclopedia of Statistical Science (LovricM., ed.). Springer, Berlin, Heidelberg, 2011, pp. 701–702. https://doi.org/10.1007/978-3-642-04898-2_319.
50.
EkiciÜ. Evaluating Rail Passengers Sector in Turkey. PhD Doctoral Program. Middle East Technical University, Ankara, Turkey, 2022.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
