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Traffic behavior during the COVID-19 pandemic and its potential consequences for passenger rail transport in the example of selected European countries

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EN
Abstrakty
EN
The COVID-19 pandemic has caused vast changes in the functioning of societies and economies, including restrictions on the use of rail transportation. As a result, the number of passengers has declined, and despite the lifting of restrictions, it is still difficult to estimate when and if passenger rail traffic will return to its pre-pandemic state. Therefore, it seems important to consider the following: how the pandemic has affected the transportation behavior patterns of residents and, above all, what should be done to encourage passengers to use rail transportation more often, which is more environmentally friendly and reduces greenhouse gas emissions. Thus, it seems important to consider what the “new normal” in rail transportation should look like. This article analyzes the number of passengers traveling by rail in eight European countries. This work considers quarterly data for 2013‒2019, combined passenger forecasts for 2020‒2021, and annual forecasts of rail passenger traffic until 2025 built using data for 2012‒2021.
Rocznik
Strony
22--34
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
  • West Pomeranian University of Technology in Szczecin, Faculty of Economics, Department of Management and Marketing 31 Janickiego St., 71-270 Szczecin, Poland
autor
  • University of Opole, Institute of Management and Quality, Department of Logistics and Marketing 45 a Ozimska St., 45-058 Opole, Poland
Bibliografia
  • 1. Ahmad, M., Franz, F., Nauclér, T. & Riefer, D. (2022) Opportunities for industry leaders as new travelers take to the skies. [Online] April 5. Available from: https://www. mckinsey.com/industries/travel-logistics-and-infrastructure/ our-insights/opportunities-for-industry-leaders-as-newtravelers-take-to-the-skies [Accessed: February 23, 2023].
  • 2. Barczak, A. (2015) Measurement of seasonal fluctuations in passenger traffic using the case study of Gdańsk airport. Folia Pomeranae Universitatis Technologiae Stetinensis, Oeconomica 321(80)3, pp. 5‒14, (in Polish).
  • 3. Barczak, A. (2016) Metoda trendów jednoimiennych okresów jako narzędzie prognozowania ruchu pasażerskiego na przykładzie Portu Lotniczego Gdańsk. In: Feliks, J. (Ed.) Wybrane Zagadnienia Logistyki Stosowanej 4, pp. 13‒23, Cracow, Poland: AGH University of Science and Technology Publisher.
  • 4. Barczak, A. (2021) COVID-19 pandemic ‒ financial consequences for Polish airports ‒ selected aspects. Aerospace 8 (11), 353, doi: 10.3390/aerospace8110353.
  • 5. Barczak, A., Dembińska, I., Rozmus, D. & Szopik-Depczyńska, K. (2022) The impact of COVID-19 pandemic on air transport passenger markets-implications for selected EU airports based on time series models analysis. Sustainability 14, 4345, doi: 10.3390/ su14074345.
  • 6. Billah, B., King, M.L., Snyder, R. & Koehler, A.B. (2006) Exponential smoothing model selection for forecasting. International Journal of Forecasting 22 (2), pp. 239‒247, doi: 10.1016/j.ijforecast.2005.08.002.
  • 7. Brown, R.G. & Meyer, R.F. (1961) The fundamental theorem of exponential smoothing. Operations Research 9 (5), pp. 603‒767, doi: 10.1287/opre.9.5.673.
  • 8. Chapuis, R., Delporte, T. & Lotz, C. (2021) Boosting passenger preference for rail. Report. Available from: https://www.mckinsey.com/industries/travel-logistics-andinfrastructure/our-insights/boosting-passenger-preference -for-rail [Accessed: February 23, 2023].
  • 9. Dean, J. (2013) Making Habits, Breaking Habits: How to Make Changes that Stick. Boston: Da Capo Press.
  • 10. European Commission (2019) Transport in the European Union. Current Trends and Issues. Available from: https:// transport.ec.europa.eu/news-events/news/transporteuropean-union-current-trends-and-issues-2019-03-13_en [Accessed: February 20, 2023].
  • 11. Eurostat (2023) Passenger transport by type of transport (detailed reporting only). [Online]. Available from: https:// ec.europa.eu/eurostat/databrowser/view/rail_pa_typepas/ default/map?lang=en [Accessed: February 23, 2023].
  • 12. Hilgert, T., von Behren, S., Eisenmann, C. & Vortisch, P. (2018) Are activity patterns stable or variable? Analysis of three-year panel data. Transportation Research Record. Journal of the Transportation Research Board 2672 (47), doi: 10.1177/0361198118773557
  • 13. Kauf, S. & Tłuczak, A. (2013) Metody i techniki badań marketingowych na przykładzie zachowań komunikacyjnych opolan. Opole, Poland: Wydawnictwo Uniwersytetu Opolskiego.
  • 14. Kovačević, S., Rebić, M. & Kurušić, D. (2021) The Impact of the COVID-19 Pandemic on the Labor Market of Bosnia and Herzegovina: Application of the Exponential Equalization Methods. Proceedings of the 8th REDETE Conference Researching Economic Development and Entrepreneurship in Transition Economies, Banja Luka, September 03‒05, 2021, pp. 106‒129.
  • 15. Loxton, M., Truskett, R., Scarf, B., Sindone, L., Baldry, G. & Zhao, Y. (2020) Consumer behaviour during crises: Preliminary research on how coronavirus has manifested consumer panic buying, herd mentality, changing discretionary spending and the role of the media in influencing behaviour. Journal of Risk and Financial Management 13 (8), 166, doi: 1911-8074/13/8/166.
  • 16. Mancuso, A.C.B. & Werner, L. (2013) Review of combining forecasts approaches. Independent Journal of Management & Production 4 (1), pp. 248‒277, doi: 10.14807/ ijmp.v4i1.59.
  • 17. Montgomery, D.C., Johnson, L.A. & Gardiner, J.S. (1990) Forecasting and Time Series Analysis. 2nd Edition. McGraw-Hill.
  • 18. Müggenburg, H., Busch-Geertsema, A. & Lanzendorf, M. (2015) Mobility biographies: A review of achievements and challenges of the mobility biographies approach and a framework for further research. Journal of Transport Geography 46, pp. 151‒163, doi: 10.1016/j.jtrangeo. 2015.06.004.
  • 19. Oral, İ.O. (2019) Comparison of the winters’ seasonality exponential smoothing method with the pegels’ classification: Forecasting of Turkey’s economic growth rates. Anadolu Üniversitesi Sosyal Bilimler Dergisi 19 (3), pp. 275‒294, doi: 10.18037/ausbd.632023.
  • 20. Ostertagova, E. & Ostertag, O. (2011) The Simple Exponential Smoothing Model. Proceedings of the 4th International Conference: Modelling of Mechanical and Mechatronic Systems 2011, September 20‒22, 2011, Herľany, Slovak Republic, pp. 380‒384.
  • 21. Ostertagova, E. & Ostertag, O. (2012) Forecasting using simple exponential smoothing method. Acta Electrotechnica et Informatica 12 (3), pp. 62–66, doi: 10.2478/v10198-012- 0034-2.
  • 22. Perzyńska, J. (2017) Combined forecasts in testing of econometric models encompassing – empirical example. Folia Pomeranae Universitatis Technologiae Stetinensis, Oeconomica 337(88)3, pp. 47‒56, doi: 10.21005/ oe.2017.88.3.05 (in Polish).
  • 23. Schäfer, M., Jaeger-Erben, M. & Bamberg, S. (2012) Life events as windows of opportunity for changing towards sustainable consumption patterns? Journal of Consumer Policy 35 (1), pp. 65‒84, doi: 10.1007/s10603-011-9181-6.
  • 24. Schellhase, R. (2000) Mobilitätsverhalten im Stadtverkehr. Eine empirische Untersuchung zur Akzeptanz verkehrspolitischer Maßnahmen. Wiesbaden: Deutscher Universitätsverlag.
  • 25. Schönfelder, S. & Axhausen, K.W. (2010) Urban rhythms and travel behaviour: spatial and temporal phenomena of daily travel. Ashgate Publishing.
  • 26. Sigurdardottir, S.B., Kaplan, S., Møller, M. & Teasdale, T.W. (2013) Understanding adolescents’ intentions to commute by car or bicycle as adults. Transportation Research Part D: Transport and Environment 24, pp. 1‒9, doi: 10.1016/j.trd.2013.04.008.
  • 27. Strelau, J. (2000) Psychologia. Podręcznik akademicki. Gdańsk, Poland: Gdańskie Wydawnictwo Psychologiczne.
  • 28. Strömberg, H. & Karlsson, I.M. (2016) Enhancing utilitarian cycling: A case study. Transportation Research Procedia 14, pp. 2352‒2361, doi: 10.1016/j.trpro.2016.05.264.
  • 29. Szołtysek, J. (2011) Kreowanie mobilności mieszkańców miast. Warszawa, Poland: Wolters Kluwer.
  • 30. Winkler, R.L. & Makridakis, S. (1983) The combination of forecasts. Journal of the Royal Statistical Society Series A (General) 146 (2), pp. 150‒157, doi: 10.2307/2982011.
  • 31. Zemlin, B. (2005) Das Entscheidungsverhalten bei der Verkehrsmittelwahl. EUL Verlag.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-79747e96-f1c7-4653-9366-e776fe8d574f
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