Analysis and forecast of passenger flows in public transport - the case of Poland

Piotr Gorzelanczyk

Państwowa Uczelnia Stanisława Staszica w Pile

Adrian Pawłowski




Abstrakt

Public transportation provides its services to both urban centers and neighboring areas in the immediate vicinity of the city. The problem of urban transportation is evident, the number of people willing to use public transportation has decreased. Therefore, there is a need to delve into the issue and conduct an analysis of the demand for urban transportation in Poland in 2000-2030, this will allow us to assess in what direction urban transportation is heading, whether there is an increase in the number of people using it, or whether there is a downward trend (Zielińska 2018).

Based on CSO statistics from 2009-2020 for the analysis of demand for public transportation in Poland, a forecast of people using public transportation was conducted using Statistica software for 2021-2030. Due to the situation with the COVID-19 pandemic, the study was conducted in 2 ways – with and without 2020.

Public transportation will make less and less profit and even losses for the next few years through rising gasoline and energy prices. Virtually in each of the provinces, and likewise throughout Poland, a decline in the number of people willing to use public transportation is evident. conclusions.

On the basis of the surveys carried out, there is a general trend that shows the current state of public transport. In most of the cases studied, a similar conclusion emerges, namely that public transport will experience a marked decline in the coming years. The number of people who want to use public transport will decrease, mainly due to the COVID-19 pandemic and people’s fears for their own safety.


Słowa kluczowe:

demand, urban transport, Poland


AL-MADANI H. 2018. Global road fatality trends’estimations based on country-wise microlevel data. Accident Analysis & Prevention, 111: 297–310. https://doi.org/10.1016/j.aap.2017.11.035
Crossref   Google Scholar

BLOOMFIELD P. 1973. An exponential model in the spectrum of a scalar time series. Biometrics, 60: 217–226. https://www.jstor.org/stable/2334533 (access 19.08.2022).
Crossref   Google Scholar

CHUDY-LASKOWSKA K. PISULA T. 2015. Forecasting the number of road accidents in Podkarpacie. Logistics, 4.   Google Scholar

CHUDY-LASKOWSKA K., PISULA T. 2014. Forecast of the number of road accidents in Poland. Logistics, 6.   Google Scholar

FISZEDER P. 2009. GARCH class models in empirical financial research. Scientific Publishers of the Nicolaus Copernicus University, Toruń.   Google Scholar

GORZELAŃCZYK P. 2022. Change in the Mobility of Polish Residents during the COVID-19 Pandemic. Communications – Scientific Letters of the University of Zilina, 24(3): A100-111. https://doi.org/10.26552/com.C.2022.3.A100-A111
Crossref   Google Scholar

GORZELAŃCZYK P., JURKOVIČ M., KALINA T., MOHANTY M. 2022. Forecasting the road accident rate and the impact of the COVID-19 on its frequency in the Polish provinces. Communications, 24(4): A216-A231. https://doi.org/10.26552/com.C.2022.4.A216-A231
Crossref   Google Scholar

GORZELAŃCZYK P., KOCZOROWSKI A. 2018a. Analysis of transport fleet maintenance costs on the example of the Municipal Transport Company, Buses. Technology, Operation, Transport Systems, 6.   Google Scholar

GORZELAŃCZYK P., KOCZOROWSKI A. 2018b. Optimization of transport fleet maintenance costs on the example of the Municipal Transport Company, Buses. Technology, Operation, Transport Systems, 6.   Google Scholar

GREGORCZYK A., SWARCEWICZ M. 2012. Analysis of variance in a repeated measures system to determine the effects of factors affecting linuron residues in soil. Polish Journal of Agronomy, 11: 15–20. https://www.iung.pl/PJA/wydane/11/PJA11_3.pdf   Google Scholar

Introduction to exponential smoothing. 2022. Simple. Blog. https://limoserviceinneworleans.com/ (access 19.08.2022).   Google Scholar

KARLAFTIS M., VLAHOGIANNI E. 2009. Memory properties and fractional integration in transportation time-series. Transportation Research. Part C: Emerging Technologies, 17: 444–453.
Crossref   Google Scholar

KUMAR S., VISWANADHAM V., BHARATHI B. 2019. Analysis of road accident. IOP Conference Series Materials Science and Engineering, 590(1): 012029. https://doi.org/10.1088/1757-899X/590/ 1/012029
Crossref   Google Scholar

LI L, SHRESTHA S., HU G. 2017. Analysis of road traffic fatal accidents using data mining techniques. IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), p. 363-370. https://doi.org/10.1109/SERA.2017.7965753
Crossref   Google Scholar

MAMCZUR M. 2020. Jak działa regresja liniowa? I czy warto ją stosować? Mirosław Mamczur. Blog o data science, AI, uczeniu maszynowym i wizualizacji danych. https://miroslawmamczur.pl/jak-dziala-regresja-liniowa-i-czy-warto-ja-stosowac/ (access 19.08.2022).   Google Scholar

MARCINKOWSKA J. 2015. Statistical methods and data mining in assessing the occurrence of syncope in the group of narrow-QRS tachycardia (AVNRT and AVRT). Medical University of Karol Marcinkowski in Poznań. Poznań. http://www.wbc.poznan.pl/Content/373785/index.pdf   Google Scholar

MCILROY R.C., PLANT K.A., HOQUE M.S., WU J., KOKWARO G.O., NAM V.H., STANTON N.A. 2019. Who is responsible for global road safety? A cross-cultural comparison ofactor maps. Accident Analysis & Prevention, 122: 8–18. https://doi.org/10.1016/j.aap.2018.09.011
Crossref   Google Scholar

MONEDEROA B.D., GIL-ALANAA L.A., MARTÍNEZAA M.C.V. 2021. Road accidents in Spain: Are they persistent? IATSS Research, 45(3): 317-325. https://doi.org/10.1016/j.iatssr.2021.01.002
Crossref   Google Scholar

MUĆK J. Ekonometria. Modelowanie szeregów czasowych. Stacjonarność. Testy pierwiastka jednostkowego. Modele ARDL. Kointegracja. http://web.sgh.waw.pl/~jmuck/Ekonometria/EkonometriaPrezentacja5.pdf (access 19.08.2022).   Google Scholar

PERCZAK G., FISZEDER P. 2014. GARCH model – using additional information on minimum and maximum prices. Bank and Credit, 2.   Google Scholar

PIŁATOWSKA M. 2012. The choice of the order of autoregression depending on the parameters of the generating model. Econometrics, 4(38).   Google Scholar

RABIEJ M. 2012. Statystyka z programem Statistica. Helion, Gliwice.   Google Scholar

SEBEGO M., NAUMANN R.B., RUDD R.A., VOETSCH K., DELLINGER A.M., NDLOVU C. 2008. The impact of alcohol and road traffic policies on crash rates in Botswana, 2004–2011: A time-series analysis. Accident Analysis & Prevention, 70: 33–39. https://doi.org/10.1016/j.aap.2014.02.017
Crossref   Google Scholar

SHETTY P., SACHIN P.C., KASHYAP V.K., MADI V. 2017. Analysis of road accidents using data mining techniques. International Research Journal of Engineering and Technology, 4.   Google Scholar

Techniki zgłębiania danych (data mining). StatSoft Polska. https://www.statsoft.pl/textbook/stathome_stat.html?https%3A%2F%2Fwww.statsoft.pl%2Ftextbook%2Fstdatmin.html (access 19.08.2022).   Google Scholar

Top Advantages and Disadvantages of Hadoop 3. DataFlair. https://data-flair.training/blogs/advantages-and-disadvantages-of-hadoop/ (access 19.08.2022).   Google Scholar

Transport pasażerów. 2021. Główny Urząd Statystyczny. Bank Danych Lokalnych. https://bdl.stat.gov.pl/bdl/dane/podgrup/tablica (access 19.08.2022).   Google Scholar

WÓJCIK A. 2014. Modele wektorowo-autoregresyjne jako odpowiedź na krytykę strukturalnych wielorównaniowych modeli ekonometrycznych. Studia Ekonomiczne, 193: 112-128.   Google Scholar

WROBEL M.S. 2017. Application of neural fuzzy systems in chemistry. PhD thesis. Uniwersytet Śląski, Katowice.   Google Scholar

ZIELIŃSKA E. 2018. Analysis of demand for urban transport in Poland. Autobusy: Technika, Eksploatacja, Systemy Transportowe, 6: 981–986.
Crossref   Google Scholar


Opublikowane
24-10-2023

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Gorzelanczyk, P., & Pawłowski, A. (2023). Analysis and forecast of passenger flows in public transport - the case of Poland. Technical Sciences, 26(26), 161–184. https://doi.org/10.31648/ts.9304

Piotr Gorzelanczyk 
Państwowa Uczelnia Stanisława Staszica w Pile
Adrian Pawłowski 




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Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa 4.0 Międzynarodowe.





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