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Exploring the Highway Travel Patterns Affected by COVID-19 through Outbreak to Recovery Stages – A Case Study in Guizhou Province
Weizheng Liu, Yanyan Chen
Keywords:COVID-19 pandemic, highway transaction dataset, travel behaviour, complex network analysis, community detection

Abstract

The examination of highway travel behaviour during the COVID-19 pandemic can provide valuable insights into the impacts of the pandemic and associated policies on human mobility patterns. This paper proposes a comprehensive examination, measurement and characterisation approach in the perspective of network and community structure. To capture the changes in travel behaviour, four stages were defined based on four consecutive Augusts from 2019 to 2022, during which varying levels of restrictions were implemented. The findings reveal interesting trends in travel patterns. In 2020, after the clearance of pandemic cases, there was a remarkable increase of over 10% in highway trips. However, in 2021, with the emergence of COVID-19 variants, there was a significant decline of over 30% in highway trips. By employing complex network analysis, key metrics of the primary network, including link weight, node flux and network connectivity, exhibited a notable decrease during the pandemic. These changes in network properties also reflect the spatial heterogeneity of highway travel demand. Moreover, the outcomes of community detection shed light on the evolution of the highway community structure, highlighting the efficacy of a community-collaboration strategy for highway management during public emergency events, as it fosters strong local interaction within the community.

References

[1] Patra SS, Chilukuri BR, Vanajakshi L. Analysis of road traffic pattern changes due to activity restrictions during COVID-19 pandemic in Chennai. Transportation Letters. 2021;13(5-6):473–481. DOI: 10.1080/19427867.2021.1899580.

[2] WHO Coronavirus (COVID-19) Dashboard. WHO (World Health Organization). https://covid19.who.int/ [Accessed 15th March 2023].

[3] Jia J, et al. Understanding bike-sharing mobility patterns in response to the COVID-19 pandemic. Cities. 2023;142:104554. DOI: 10.1016/j.cities.2023.104554.

[4] Chen X, et al. Clustering characteristics of COVID-19 cases and influencing factors in Chongqing Municipality. Progress in Geography. 2020;29(11):1798–1808. DOI: 10.18306/dlkxjz.2020.11.002.

[5] Tao Y. Maximum entropy method for estimating the reproduction number: An investigation for COVID-19 in China and the United States. Physical Review E. 2020;102(3):032136. DOI: 10.1103/PhysRevE.102.032136.

[6] Jia XL, Zhou WX, Han JX. Blocking effects of traffic control measures on COVID-19 transmission in city territories. China J. Highway Transport. 2022;35(01):252–262. DOI: 10.19721/j.cnki.1001-7372.2022.01.022.

[7] Jia J, Zhang H, Shi B. Uncovering taxi mobility patterns associated with the public transportation shutdown using multisource data in Washington, D.C. KSCE J Civ Eng. 2022;26:5291–5300. DOI: 10.1007/s12205-022-0434-5.

[8] Zhang W, et al. Structural changes in intercity mobility networks of China during the COVID-19 outbreak: A weighted stochastic block modeling analysis. Computers, Environment and Urban Systems. 2022;96:101846. DOI: 10.1016/j.compenvurbsys.2022.101846.

[9] Li Q, et al. How does COVID-19 affect traffic on highway network: Evidence from Yunnan Province, China. Journal of Advanced Transportation. 2022;2022:7379334. DOI: 10.1155/2022/7379334.

[10] Ma Y, Xu J, Gao C, Tong X. Impacts of COVID-19 travel restriction policies on the traffic quality of the national and provincial trunk highway network: A case study of Shaanxi Province. International Journal of Environmental Research and Public Health. 2022;19(15):9387. DOI: 10.3390/ijerph19159387.

[11] Parr S, et al. Traffic impacts of the COVID-19 pandemic: Statewide analysis of social separation and activity restriction. Natural Hazards Review. 2020;21(3):04020025. DOI: 10.1061/(ASCE)NH.1527-6996.0000409.

[12] Cui Z, et al. How does COVID-19 pandemic impact cities' logistics performance? An evidence from China's highway freight transport. Transport Policy. 2022;120:11–22. DOI: 10.1016/j.tranpol.2022.03.002.

[13] Gu M, Sun S, Jian F, Liu, X. Analysis of changes in intercity highway traffic travel patterns under the impact of COVID-19. Journal of Advanced Transportation. 2021;1–9. DOI: 10.1155/2021/7709555.

[14] EastMoney. The spread of ETC in China. 2021. http://finance.eastmoney.com/a/202103281861734043.html [Accessed 4th Sep. 2023].

[15] Akhtar M, Moridpour S. A review of traffic congestion prediction using artificial intelligence. Journal of Advanced Transportation. 2021;1–18. DOI: 10.1155/2021/8878011.

[16] Cao W, Wang J. Research on traffic flow congestion based on Mamdani fuzzy system. AIP Conference Proceedings, 19-20 Jan. 2019, Wuhan, China. 2019;2073(1):020101. DOI: 10.1063/1.5090755.

[17] Wen F, et al. A hybrid temporal association rules mining method for traffic congestion prediction. Computers & Industrial Engineering. 2019;130:779–787. DOI: 10.1016/j.cie.2019.03.020.

[18] Adetiloye T, Awasthi A. Multimodal big data fusion for traffic congestion prediction. Multimodal Analytics for Next-Generation Big Data Technologies and Applications. 2019;319–335, DOI: 10.1007/978-3-319-97598-6_13.

[19] Jia J, et al. Exploring the individual travel patterns utilizing large-scale highway transaction dataset. Sustainability. 2022;14(21):14196. DOI: 10.3390/su142114196.

[20] Yang Q, et al. Urban traffic congestion prediction using floating car trajectory data. Algorithms and Architectures for Parallel Processing: 15th International Conference, ICA3PP 2015, 18–20 Nov. 2015, Zhangjiajie, China. 2015. p. 18–30. DOI: 10.1007/978-3-319-27122-4_2.

[21] Kong X, et al. Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Generation Computer Systems. 2016;61:97–107. DOI: 10.1016/j.future.2015.11.013.

[22] Fu X, et al. Spatial heterogeneity and migration characteristics of traffic congestion—A quantitative identification method based on taxi trajectory data. Physica A: Statistical Mechanics and its Applications. 2022;588:126482. DOI: 10.1016/j.physa.2021.126482.

[23] Huang Z, et al. A peak traffic congestion prediction method based on bus driving time. Entropy. 2019;21(7):709. DOI: 10.3390/e21070709.

[24] Li S, Zhang J, Zhong G, Ran B. A simulation approach to detect arterial traffic congestion using cellular data. Journal of Advanced Transportation. 2022;2022:1–13. DOI: 10.1155/2022/8811139.

[25] Yan X, et al. Revealing spatiotemporal matching patterns between traffic flux and road resources using big geodata – A case study of Beijing. Cities. 2022;127:103754. DOI: 10.1016/j.cities.2022.103754.

[26] Jia JS, et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 2020;582:389–394. DOI: 10.1038/s41586-020-2284-y.

[27] Benita F. Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis. Sustainable Cities and Society. 2021;70:102916. DOI: 10.1016/j.scs.2021.102916.

[28] Beck MJ, Hensher, DA. Insights into the impact of COVID-19 on household travel and activities in Australia – The early days under restrictions. Transport Policy. 2020;96:76–93. DOI: 10.1016/j.tranpol.2020.07.001.

[29] Parker ME, et al. Public transit use in the United States in the era of COVID-19: Transit riders’ travel behavior in the COVID-19 impact and recovery period. Transport Policy. 2021;111:53–62. DOI: 10.1016/j.tranpol.2021.07.005.

[30] Parr S, et al. Traffic impacts of the COVID-19 pandemic: Statewide analysis of social separation and activity restriction. Natural Hazards Review. 2020;21(3):04020025. DOI: 10.1061/(ASCE)NH.1527-6996.0000409.

[31] He L, et al. Inter-city transportation demand under the COVID-19 pandemic. Urban Transp. China. 2020;18:51–61.

[32] Wang D, et al. Impact of COVID-19 behavioral inertia on reopening strategies for New York City transit. International Journal of Transportation Science and Technology. 2021;10(2):197–211. DOI: 10.1016/j.ijtst.2021.01.003.

[33] Saberi M, et al. Understanding the impacts of a public transportation disruption on bicycle sharing mobility patterns: A case of Tube strike in London. Journal of Transport Geography. 2018;66:154–166. DOI: 10.1016/j.jtrangeo.2017.11.018.

[34] Chen Y, Sun X, Deveci M, Coffman DM. The impact of the COVID-19 pandemic on the behaviour of bike sharing users. Sustainable Cities and Society. 2022;84:104003. DOI: 10.1016/j.scs.2022.104003.

[35] Fishman E, Washington S, Haworth N, Watson A. Factors influencing bike share membership: An analysis of Melbourne and Brisbane. Transportation Research Part A. 2015;71:17–30. DOI: 10.1016/j.tra.2014.10.021.

[36] Feng T, Zhang J. Multicriteria evaluation on accessibility-based transportation equity in road network design problem. Journal of Advanced Transportation. 2014;48(6):526–541. DOI: 10.1002/atr.1202.

[37] Chen G, Viana AC, Fiore M, Sarraute C. Complete trajectory reconstruction from sparse mobile phone data. EPJ Data Science. 2019;8(1):1–24. DOI: 10.1140/epjds/s13688-019-0206-8.

[38] Fortunato S. Community detection in graphs. Physics Reports. 2010;486(3–5):75–174. DOI: 10.1016/j.physrep.2009.11.002.

[39] Newman ME, Girvan M. Finding and evaluating community structure in networks. Physical Review E. 2004;69(2):026113. DOI: 10.1103/PhysRevE.69.026113.
Published
20.06.2024
Copyright (c) 2023 Weizheng Liu, Yanyan Chen

Published by
University of Zagreb, Faculty of Transport and Traffic Sciences
Online ISSN
1848-4069
Print ISSN
0353-5320
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