Shifting the population mobility of the Ukraine western region on the strength of the COVID-19 pandemic


  • Halyna Pivtorak Department of Transport Technologies, Lviv Polytechnic National University, Lviv Author
  • Mykola Zhuk Department of Transport Technologies, Lviv Polytechnic National University, Lviv Author
  • Ivanna Gits Department of Transport Technologies, Lviv Polytechnic National University, Lviv Author
  • Andrii Galkin Department of Transport System and Logistic, O. M. Beketov National University of Urban Economyin Kharkiv, Kharkiv, Ukraine Author



intercity mobility, transport modelling, Covid-19 pandemic, passenger flows, mode choice


The Covid-19 pandemic has significantly affected the economic and social spheres of all countries. Restrictions introduced to reduce the risk of transmission have changed the structure of population movements. The impact of these restrictions on the characteristics of intercity travel is still an understudied problem. Based on the analysis of statistical data and the results of questionnaires, the article assesses the impact of pandemic restrictions on population mobility in the Western region of Ukraine and changes in the distribution of passenger flows between different modes (bus, rail, private transport, joint travel). In 2020, the volume of passenger traffic in the region decreased by an average of half compared to the previous year. The decline is sharper for rail passenger transport compared to the bus transport. For more developed railway networks, the impact of the pandemic on passenger traffic is more pronounced. Quarantine restrictions have also increased the share of own car travel. According to research, the distribution of intercity trips between modes is influenced by the age and sex of the traveler. During the pandemic, users of transport services who travel with children under the age of 14 choose private transport to travel more often than those who travel alone. The degree of influence of the above factors on the distribution of modes depends on the length of the trip. The application part of the work presents the results of modeling passenger flows of the studied region in the software environment PTV Visum. It was found that at the beginning of the quarantine restrictions the number of intercity trips decreases sharply. As the duration of restrictions increases, the rate of decline in mobility decreases. These data can be further taken into account when planning the work of transport enter-prises and meeting the population`s demand for travel. The practical application of the study results is that the identification of trends in the mobility of residents of the studying region depending on the impact of pandemic restrictions allows you to predict the mode and type of vehicles used. Based on these data, you can determine marketing strategies for the development of certain modes and directions of transportation.


Aletta, F., Brinchi, S., Carrese, S., & Gemma, A., Guattari, C., Mannini, L. & Patella, S. (2020). Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease. Noise Mapping. 7. 114-122. DOI: 10.1515/noise-2020-0010.

Aloi, A., Alonso, B., Benavente, J., Cordera, R., Echániz, E., González, F., Ladisa, C., Lezama-Romanelli, R., López-Parra, Á., Mazzei, V., Perrucci, L., Prieto-Quintana, D., Rodríguez, A. & Sañudo, R. (2020). Effects of the COVID-19 Lockdown on Urban Mobility: Empirical Evidence from the City of Santander (Spain). Sustainability. 12(9):3870. DOI:10.3390/su12093870.

Arbués, P., Baños, J.F., Mayor, M. & Suárez, P. (2016). Determinants of ground transport modal choice in long-distance trips in Spain. Transportation Research Part A: Policy and Practice. 84, 131-143.

Aultman-Hall, L., & Ullman, H. (2020). Long-distance and intercity travel: who participates in global mobility? In Mapping the Travel Behavior Genome (pp. 187-207). Elsevier.

Barbieri, D.M., Lou, B., Passavanti, M., Hui, C., Hoff, I., Lessa, D.A., et al. (2021) Impact of COVID-19 pandemic on mobility in ten countries and associated perceived risk for all transport modes. PLoS ONE 16(2): e0245886. DOI: 10.1371/journal.pone.0245886.

Breyer, N., Gundlegård, D., & Rydergren, C. (2021). Travel mode classification of intercity trips using cellular network data. Transportation Research Procedia, 52, 211-218.

Cartenì, A., Di Francesco, L., Henke, I., Marino, T. & Falanga, A. (2021). The Role of Public Transport during the Second COVID-19 Wave in Italy. Sustainability. 13. 11905.10.3390/su132111905.

Christidis, P., Christodoulou, A., Navajas-Ca-wood, E. & Ciuffo, B. The Post-Pandemic Recovery of Transport Activity: Emerging Mobility Patterns and Repercussions on Future Evolution. Sustainability. 2021; 13(11):6359. DOI: 10.3390/su13116359.

Bauer, M., Dźwigoń, W., & Richter, M. (2021). Personal safety of passengers during the first phase Covid-19 pandemic in the opinion of public transport drivers in Krakow. Archives of Transport, 59(3), 41-55.

Davidich, N., Galkin, A., Sabadash, V., Chumachenko, I., Melenchuk, T. & Davidich, Y. (2020). Projecting of urban transport infrastructure considering the human factor. Communications-Scientific letters of the University of Zilina, 22(1), 84-94. DOI:10.26552/com.C.2020.1.84-94.

de Haas, M., Faber, R. & Hamersma, M. (2020). How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transportation Research Interdisciplinary Perspectives, Volume 6, 100150. DOI: 10.1016/j.trip.2020.100150.

Hörcher, D., Singh, R. & Graham, D.J. (2020). Social Distancing in Public Transport: Mobilising New Technologies for Demand Management under the COVID-19 Crisis. SSRN. DOI:10.2139/ssrn.3713518.

Hui, K.T.Y., Wang, C., Kim, A. & Qiu, T.Z. (2017). Investigating the use of anonymous cellular phone data to determine intercity travel volumes and modes (No. 17-03652). Transportation Research Board 96th Annual Meeting, Washington DC, United State.

Jachimowski, R., Kłodawski, M., Lewczuk, K., Szczepański, E. & Wasiak, M. (2015). Implementation of the model of proecological transport system. Journal of KONES. Powertrain and Transport. 20(4). 129-139. DOI:10.5604/12314005.1137402.

Jacyna-Gołda, I., Zak, J. & Gołębiowski, P. (2015). Models of traffic flow distribution for various scenarios of the development of proecological transport system. Archives of Transport. 32. 17-28. DOI:10.5604/08669546.1146994.

Janzen, M., Vanhoof, M., Smoreda, Z. & Axhausen, K. W. (2018). Closer to the total? Long-distance travel of French mobile phone users. Travel Behaviour and Society, 11, 31-42.

Jenelius, E. & Cebecauer, M. (2020). Impacts of COVID-19 on public transport rider ship in Sweden: Analysis of ticket validations, sales and passenger counts. Transportation Research Interdisciplinary Perspectives, 8, 100242. DOI:10.1016/j.trip.2020.100242.

Kiashemshaki, M., Huang, Z. & Saramäki, J. (2022). Mobility Signatures: A Tool for Characterizing Cities Using Intercity Mobility Flows. Frontiers in big data, 5, 822889. DOI:10.3389/fdata.2022.822889.

Li, T., Wang, J., Huang, J., Yang, W. & Chen, Z. (2021). Exploring the dynamic impacts of COVID-19 on intercity travel in China. Journal of Transport Geography, Volume 95, 103153. DOI: 10.1016/j.jtrangeo.2021.103153.

Li, X., Tang, J., Hu, X. & Wang, W. (2020) Assessing intercity multimodal choice behavior in a Touristy City: A factor analysis. Journal of Transport Geography, 86, 102776.

Li, X., Zhang, S., Wu, Y., Wang, Y. & Wang, W. (2021). Exploring Influencing Factors of Intercity Mode Choice from View of Entire Travel Chain. Journal of Advanced Transportation, vol. 2021, 13 pages. DOI:10.1155/2021/9454873.

Matthew J. B., David A. H. & John D. N. (2021). Public transport trends in Australia during the COVID-19 pandemic: An investigation of the influence of bio-security concerns on trip behaviour. Journal of Transport Geography, Volume 96, 103167. DOI: 10.1016/j.jtrangeo.2021.103167.

Ortuzar, J.D. & Willumsen, L.G. (2001). Modelling transport. (3rd ed.). Chichester: Wiley. [24] Pivtorak, H. (2021). Determination of parameters of urban passenger transportation network based on models of random utility theory (Vyznachennia parametriv merezhi miskykh pasazhyrskykh perevezen na osnovi modelei teorii korysnosti z vypadkovym vyborom). PhD thesis. Lviv, 202 p. (in Ukrainian).

Questionnaires of transport services users:

State Statistics Service of Ukraine.

Statistical collection «Transport of Ukraine. 2019». (2020). State Statistics Service of Ukraine. Кyiv. 115 p. (in Ukrainian).

Statistical collection «Transport and communication of Ukraine. 2018». (2019). State Statistics Service of Ukraine. Кyiv. 154 (in Ukrainian).

Tirachini, A. & Cats, O. (2020). COVID-19 and public transportation: Current assessment, prospects, and research needs. Journal of Public Transportation. 22(1). DOI: 10.5038/23750901.22.1.1.

Vakulenko, K., Kuhtin, K., Afanasieva, I. & Galkin, A. (2019). Designing optimal public bus route networks in a suburban area. Transportation Research Procedia, 39, 554-564. DOI: 10.1016/j.trpro.2019.06.057.

Wielechowski, M., Czech, K. & Grzęda, Ł. 2020. Decline in Mobility: Public Transport in Poland in the time of the COVID-19 Pandemic. Economies 8, no. 4: 78. DOI:10.3390/economies8040078.

Zhang, R., Pan, J., & Lai, J. (2021). Network Structure of Intercity Trips by Chinese Residents under Different Travel Modes: A Case Study of the Spring Festival Travel Rush. Complexity, 2021.


2022-06-30 — Updated on 2022-06-30



Original articles

How to Cite

Pivtorak, H., Zhuk, M., Gits, I., & Galkin, A. (2022). Shifting the population mobility of the Ukraine western region on the strength of the COVID-19 pandemic. Archives of Transport, 62(2), 7-32.


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