Analysis and forecast of passenger flows in public transport - the case of Poland
Piotr Gorzelanczyk
Państwowa Uczelnia Stanisława Staszica w PileAdrian 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, PolandBibliografia
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
Państwowa Uczelnia Stanisława Staszica w Pile
