Share:


Pandemic impact on traffic trends and patterns in the city of Belgrade

    Draženko Glavić Affiliation
    ; Ana Trpković Affiliation
    ; Marina Milenković Affiliation
    ; Sreten Jevremović Affiliation

Abstract

The appearance of the COVID-19 virus has caused great changes in all spheres of life. Probably the most visible change is the cities’ lockdown, with the suspension of traffic and transport systems. The capital of the Serbia – Belgrade also went through a complete lockdown, which lasted for almost 2 months (53 days). In that period, nearly all activities were reduced, producing significant losses for the whole economic development, healthcare, food supply chain, transport sector and most importantly public transport system. The behaviour of users in such situations can greatly influence the change in the share of certain modes of transport in the overall modal share. The aim of this article is to examine the influence of the COVID-19 pandemic on the transport mode choice for different trip purposes, as well as the examination of different impact factors, such as gender, age, education level, employment status, income, transport mode used before the pandemic, and average distance travelled, on the change of mode of transport. Data of 1143 users were analysed through a survey, for the area of the city of Belgrade, using the McNemar–Bowker test and binary logistic regression. The results showed that pandemic had a significant impact on the transport mode change for all trip purposes. The key factors influencing the change in the mode of transport are factors related to gender, level of education, income, the type of transport used before the pandemic and the average distance travelled. It is also interesting to note that the results showed a significant number of transfers to individual modes of transport, as well as micromobility vehicles and walking. Therefore, this article provides the necessary help in understanding the transport system user’s behaviour, which can facilitate the choice of adequate measures, modes and activities for decision-makers in these specific situations.

Keyword : COVID-19, impact factors, modal share, transport mode change, user behaviour, user attitudes, safety perception

How to Cite
Glavić, D., Trpković, A., Milenković, M., & Jevremović, S. (2023). Pandemic impact on traffic trends and patterns in the city of Belgrade. Transport, 38(3), 165–177. https://doi.org/10.3846/transport.2023.19375
Published in Issue
Dec 21, 2023
Abstract Views
250
PDF Downloads
274
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abdullah, M.; Dias, C.; Muley, D.; Shahin, M. 2020. Exploring the impacts of COVID-19 on travel behavior and mode preferences, Transportation Research Interdisciplinary Perspectives 8: 100255. https://doi.org/10.1016/j.trip.2020.100255

Agüero, F.; Adell, M. N.; Giménez, A. P.; López Medina, M. J.; Continente, X. G. 2011. Adoption of preventive measures during and after the 2009 influenza A (H1N1) virus pandemic peak in Spain, Preventive Medicine 53(3): 203–206. https://doi.org/10.1016/j.ypmed.2011.06.018

Anke, J.; Francke, A.; Schaefer, L.-M.; Petzoldt, T. 2021. Impact of SARS-CoV-2 on the mobility behaviour in Germany, European Transport Research Review 13: 10. https://doi.org/10.1186/s12544-021-00469-3

Bajčetić, S.; Tica, S.; Živanović, P.; Milovanović, B.; Đorojević, A. 2018. Analysis of public transport users’ satisfaction using quality function deployment: Belgrade case study, Transport 33(3): 609–618. https://doi.org/10.3846/transport.2018.1570

Beck, M. J.; Hensher, D. A. 2020a. Insights into the impact of COVID-19 on household travel and activities in Australia – the early days of easing restrictions, Transport Policy 99: 95–119. https://doi.org/10.1016/j.tranpol.2020.08.004

Beck, M. J.; Hensher, D. A. 2020b. Insights into the impact of COVID-19 on household travel and activities in Australia – the early days under restrictions, Transport Policy 96: 76–93. https://doi.org/10.1016/j.tranpol.2020.07.001

Beck, M. J.; Hensher, D. A.; Wei, E. 2020. Slowly coming out of COVID-19 restrictions in Australia: Implications for working from home and commuting trips by car and public transport, Journal of Transport Geography 88: 102846. https://doi.org/10.1016/j.jtrangeo.2020.102846

Bucsky, P. 2020. Modal share changes due to COVID-19: the case of Budapest, Transportation Research Interdisciplinary Perspectives 8: 100141. https://doi.org/10.1016/j.trip.2020.100141

Dam, P.; Mandal, S.; Mondal, R.; Sadat, A.; Chowdhury, S. R.; Mandal, A. K. 2020. COVID-19: impact on transport and mental health, Journal of Transport & Health 19: 100969. https://doi.org/10.1016/j.jth.2020.100969

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 6: 100150. https://doi.org/10.1016/j.trip.2020.100150

Durnin, M. 2020. COVID-19 Update: China Survey Results. British Council, UK. Available from Internet: https://opportunities-insight.britishcouncil.org/insights-blog/covid-19-update-china-survey-results

Eisenmann, C.; Nobis, C.; Kolarova, V.; Lenz, B.; Winkler, C. 2021. Transport mode use during the COVID-19 lockdown period in Germany: the car became more important, public transport lost ground, Transport Policy 103: 60–67. https://doi.org/10.1016/j.tranpol.2021.01.012

Glavić, D.; Milenković, M. 2019. Electric micro mobility vehicles technologies, opportunities, assessment and forecast, in 7th International Conference “Towards a Humane City: Environmentally Friendly Mobility”, 6–7 December 2019, Novi Sad, Serbia, 199–205.

Halldorsdottir, K.; Christensen, L.; Jensen, T. C.; Prato, C. G. 2011. Modelling mode choice in short trips: shifting from car to bicycle, in European Transport Conference 2011, 10–12 October 2011, Glasgow, Scotland, 1–21. Available from Internet: https://aetransport.org/public/downloads/Tylq7/4904-514ec5fd34e78.pdf

Hattrup-Silberberg, M.; Hausler, S.; Heineke, K.; Laverty, N.; Möller, T.; Schwedhelm, D.; Wu, T. 2020. Five COVID-19 after Shocks Reshaping Mobility’s Future. McKinsey & Company. Available from Internet: https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/five-covid-19-aftershocks-reshaping-mobilitys-future

Heinen, E.; Chatterjee, K. 2015. The same mode again? An exploration of mode choice variability in Great Britain using the national travel survey, Transportation Research Part A: Policy and Practice 78: 266–282. https://doi.org/10.1016/j.tra.2015.05.015

Hong, J.; McArthur, D.; Raturi, V. 2020. Did safe cycling infrastructure still matter during a COVID-19 lockdown?, Sustainability 12(20): 8672. https://doi.org/doi:10.3390/su12208672

Hudda, N.; Simon, M. C.; Patton, A. P.; Durant, J. L. 2020. Reductions in traffic-related black carbon and ultrafine particle number concentrations in an urban neighborhood during the COVID-19 pandemic, Science of the Total Environment 742: 140931. https://doi.org/10.1016/j.scitotenv.2020.140931

Jenelius, E.; Cebecauer, M. 2020. Impacts of COVID-19 on public transport ridership in Sweden: analysis of ticket validations, sales and passenger counts, SSRN 2020: 3641536. https://doi.org/10.2139/ssrn.3641536

Lee, H.; Park, S. J.; Lee, G. R.; Kim, J. E.; Lee, J. H.; Jung, Y.; Nam, E. W. 2020. The relationship between trends in COVID-19 prevalence and traffic levels in South Korea, International Journal of Infectious Diseases 96: 399–407. https://doi.org/10.1016/j.ijid.2020.05.031

Lemke, M. K.; Apostolopoulos, Y.; Sönmez, S. 2020. Syndemic frameworks to understand the effects of COVID-19 on commercial driver stress, health, and safety, Journal of Transport & Health 18: 100877. https://doi.org/10.1016/j.jth.2020.100877

Li, X.; Farrukh, M.; Lee, C.; Khreis, H.; Sarda, S.; Sohrabi, S.; Zhang, Z.; Dadashova, B. 2022. COVID-19 impacts on mobility, environment, and health of active transportation users, Cities 131: 103886. https://doi.org/10.1016/j.cities.2022.103886

Ma, S.; Yu, Z.; Liu, C. 2020. Nested logit joint model of travel mode and travel time choice for urban commuting trips in Xi’an, China, Journal of Urban Planning and Development 146(2): 04020020. https://doi.org/10.1061/(asce)up.1943-5444.0000574

Mars, L.; Arroyo, R.; Ruiz, T. 2022. Mobility and wellbeing during the COVID-19 lockdown. Evidence from Spain, Transportation Research Part A: Policy and Practice 161: 107–129. https://doi.org/10.1016/j.tra.2022.05.004

Mathew, J. K.; Liu, M.; Seeder, S.; Li, H.; Bullock, D. M. 2019. Analysis of e-scooter trips and their temporal usage patterns, ITE Journal 89(6): 44–49.

Milenković, M.; Glavić, D.; Maričić, M. 2019. Determining factors affecting congestion pricing acceptability, Transport Policy 82: 58–74. https://doi.org/10.1016/j.tranpol.2019.08.004

Mirsoleymani, S.; Nekooghadam, S. M. 2020. Risk factors for severe coronavirus disease 2019 (COVID-19) among Iranian patients: who was more vulnerable?, SSRN 2020: 3566216. https://doi.org/10.2139/ssrn.3566216

Musselwhite, C.; Avineri, E.; Susilo, Y. 2020. Editorial JTH 16 –The Coronavirus Disease COVID-19 and implications for transport and health, Journal of Transport & Health 16: 100853. https://doi.org/10.1016/j.jth.2020.100853

Orro, A.; Novales, M.; Monteagudo, Á.; Pérez-López, J.-B.; Bugarín, M. R. 2020. Impact on City bus transit services of the COVID–19 lockdown and return to the new normal: the case of a Coruña (Spain), Sustainability 12(17): 7206. https://doi.org/10.3390/su12177206

PBOT. 2018. 2018 E-Scooter Findings Report. Portland Bureau of Transportation (PBOT), Portland, OR, US. 36 p. Available from Internet: https://www.portland.gov/sites/default/files/2020-04/pbot_e-scooter_01152019.pdf

Scheiner, J. 2010. Interrelations between travel mode choice and trip distance: trends in Germany 1976–2002, Journal of Transport Geography 18(1): 75–84. https://doi.org/10.1016/j.jtrangeo.2009.01.001

Sharifi, A.; Khavarian-Garmsir, A. R. 2020. The COVID-19 pandemic: impacts on cities and major lessons for urban planning, design, and management, Science of the Total Environment 749: 142391. https://doi.org/10.1016/j.scitotenv.2020.142391

Stavrinos, D.; McManus, B.; Mrug, S.; He, H.; Gresham, B.; Albright, M. G.; Svancara, A. M.; Whittington, C.; Underhill, A.; White, D. M. 2020. Adolescent driving behavior before and during restrictions related to COVID-19, Accident Analysis & Prevention 144: 105686. https://doi.org/10.1016/j.aap.2020.105686

Sui, Y.; Zhang, H.; Shang, W.; Sun, R.; Wang, C.; Ji, J.; Song, X.; Shao, F. 2020. Mining urban sustainable performance: spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future, Applied Energy 280: 115966. https://doi.org/10.1016/j.apenergy.2020.115966

Susnienė, D. 2012. Quality approach to the sustainability of public transport, Transport 27(1): 102–110. https://doi.org/10.3846/16484142.2012.668711

Teixeira, J. F.; Lopes, M. 2020. The link between bike sharing and subway use during the COVID-19 pandemic: the case-study of New York’s Citi Bike, Transportation Research Interdisciplinary Perspectives 6: 100166. https://doi.org/10.1016/j.trip.2020.100166

Trpković, A.; Stanić, B.; Tica, S.; Jevremović, S.; Živanović, P. 2019. Micromobility revolution – challenges and potentials, in 7th International Conference “Towards a Humane City: Environmentally Friendly Mobility”, 6–7 December 2019, Novi Sad, Serbia, 231–237.

Zafri, N. M.; Khan, A.; Jamal, S.; Alam, B. M. 2021. Impacts of the COVID-19 pandemic on active travel mode choice in Bangladesh: A study from the perspective of sustainability and new normal situation, Sustainability 13(12): 6975. https://doi.org/10.3390/su13126975