Nonlinear Effects of the Built Environment on Subways at Station Level: Average Travel Distance Changes under the Influence of COVID-19
Publication: Journal of Urban Planning and Development
Volume 151, Issue 1
Abstract
The travel behavior of subway passengers has shown systematic differences with the onset and progression of the COVID-19 pandemic. This study focused on subway stations in Xi’an and systematically analyzed the spatiotemporal features of the average travel distance of passengers (ATDP) during the peri- and post-COVID-19 periods. Using the gradient boost regression tree analysis framework, the study explored the nonlinear relationship between various built environment factors and changes in ATDP during pandemic periods. The results indicated that the distance to the city center, land-use mix, and road network density contributed significantly more than other variables in different periods. Furthermore, the study found that the ATDP in subway stations does not vary significantly based on the structural characteristics of the station network. These findings hold significant value for operational organizers seeking to comprehend the station-level changes in travel distances under the influence of public health crises, offering effective scientific data support for the rational development of operational plans to balance the distribution of operational capacities.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The data sets used during the current study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 72271020 and 71871027).
Author contributions: Peikun Li: Conceptualization and methodology, Writing—original draft preparation; Xumei Chen: Conceptualization and methodology, Software, Validation, Formal analysis, Funding acquisition; Hao Wang: Writing—original draft preparation; Wenbo Lu: Visualization, Writing—review and editing; Yuqing Wang: Visualization, Writing—review and editing.
References
Beck, M. J., and D. A. Hensher. 2020. “Insights into the impact of COVID-19 on household travel and activities in Australia—The early days of easing restrictions.” Transp. Policy 99: 95–119. https://doi.org/10.1016/j.tranpol.2020.08.004.
Bergantino, A. S., M. Intini, and L. Tangari. 2021. “Influencing factors for potential bike-sharing users: An empirical analysis during the COVID-19 pandemic.” Res. Transp. Econ. 86: 101028. https://doi.org/10.1016/j.retrec.2020.101028.
Bonaccorsi, G., F. Pierri, M. Cinelli, A. Flori, A. Galeazzi, F. Porcelli, A. L. Schmidt, C. M. Valensise, A. Scala, and W. Quattrociocchi. 2020. “Economic and social consequences of human mobility restrictions under COVID-19.” Proc. Natl. Acad. Sci. USA 117 (27): 15530–15535. https://doi.org/10.1073/pnas.2007658117.
Borkowski, P., M. Jażdżewska-Gutta, and A. Szmelter-Jarosz. 2021. “Lockdowned: Everyday mobility changes in response to COVID-19.” J. Transp. Geogr. 90: 102906. https://doi.org/10.1016/j.jtrangeo.2020.102906.
Chang, H.-H., B. Lee, F.-A. Yang, and Y.-Y. Liou. 2021. “Does COVID-19 affect metro use in Taipei?” J. Transp. Geogr. 91: 102954. https://doi.org/10.1016/j.jtrangeo.2021.102954.
Chang, T., D. Yang, Y. Yang, J. Huo, G. Wang, and C. Xiong. 2018. “Impact of urban rail transit on business districts based on time distance: Urumqi light rail.” J. Urban Plann. Dev. 144 (3): 18–24. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000464.
Chen, E., Z. Ye, and H. Bi. 2019. “Incorporating smart card data in spatio-temporal analysis of metro travel distances.” Sustainability 11 (24): 7069. https://doi.org/10.3390/su11247069.
Chen, F., J. Wu, X. Chen, and J. Wang. 2017. “Vehicle kilometers traveled reduction impacts of transit-oriented development: Evidence from Shanghai city.” Transp. Res. Part D Transp. Environ. 55: 227–245. https://doi.org/10.1016/j.trd.2017.07.006.
Chen, K., and R. Steiner. 2022. “Longitudinal and spatial analysis of Americans’ travel distances following COVID-19.” Transp. Res. Part D Transp. Environ. 110: 103414. https://doi.org/10.1016/j.trd.2022.103414.
Choi, J., Y. J. Lee, T. Kim, and K. Sohn. 2012. “An analysis of metro ridership at the station-to-station level in Seoul.” Transportation 39 (3): 705–722. https://doi.org/10.1007/s11116-011-9368-3.
Choi, K. 2018. “The influence of the built environment on household vehicle travel by the urban typology in Calgary, Canada.” Cities 75: 101–110. https://doi.org/10.1016/j.cities.2018.01.006.
Cui, Q., L. He, Y. Liu, Y. Zheng, W. Wei, B. Yang, and M. Zhou. 2021. “The impacts of COVID-19 pandemic on China’s transport sectors based on the CGE model coupled with a decomposition analysis approach.” Transp. Policy 103: 103–115. https://doi.org/10.1016/j.tranpol.2021.01.017.
De Haas, M., R. Faber, and M. Hamersma. 2020. “How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in The Netherlands.” Transp. Res. Interdiscip. Perspect. 6: 100150. https://doi.org/10.1016/j.trip.2020.100150.
Ding, C., T. Liu, X. Cao, and L. Tian. 2022. “Illustrating nonlinear effects of built environment attributes on housing renters'transit commuting.” Trans. Res. Part D: Trans. Environ. 112: 103503. https://doi.org/10.1016/j.trd.2022.103503.
Eliasson, J. 2022. “Will we travel less after the pandemic?” Transp. Res. Interdiscip. Perspect. 13: 100509. https://doi.org/10.1016/j.trip.2021.100509.
Gan, Z., T. Feng, Y. Wu, M. Yang, and H. Timmermans. 2019. “Station-based average travel distance and its relationship with urban form and land use: An analysis of smart card data in Nanjing city, China.” Transp. Policy 79: 137–154. https://doi.org/10.1016/j.tranpol.2019.05.003.
Gan, Z., M. Yang, T. Feng, and H. J. Timmermans. 2020. “Examining the relationship between built environment and metro ridership at station-to-station level.” Transp. Res. Part D Transp. Environ. 82: 102332. https://doi.org/10.1016/j.trd.2020.102332.
Heiler, G., T. Reisch, J. Hurt, M. Forghani, A. Omani, A. Hanbury, and F. Karimipour. 2020. “Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic.” In Proc., 2020 IEEE Int. Conf. on Big Data (Big Data), 3123–3132. New York: IEEE.
Huang, J., D. Levinson, J. Wang, J. Zhou, and Z.-j. Wang. 2018. “Tracking job and housing dynamics with smartcard data.” Proc. Natl. Acad. Sci. USA 115 (50): 12710–12715. https://doi.org/10.1073/pnas.1815928115.
Iseki, H., C. Liu, and G. Knaap. 2018. “The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington DC metrorail system.” Transp. Res. Part A Policy Pract. 116: 635–649. https://doi.org/10.1016/j.tra.2018.06.011.
Jiang, S., and C. Cai. 2022. “Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China.” Transp. Policy 127: 158–170. https://doi.org/10.1016/j.tranpol.2022.09.002.
Khan, M., K. M. Kockelman, and X. Xiong. 2014. “Models for anticipating non-motorized travel choices, and the role of the built environment.” Transp. Policy 35: 117–126. https://doi.org/10.1016/j.tranpol.2014.05.008.
Kim, J., and D. Brownstone. 2013. “The impact of residential density on vehicle usage and fuel consumption: Evidence from national samples.” Energy Econ. 40: 196–206. https://doi.org/10.1016/j.eneco.2013.06.012.
Kim, K. 2021. “Impact of COVID-19 on usage patterns of a bike-sharing system: Case study of Seoul.” J. Transp. Eng. Part A. Syst. 147 (10): 05021006. https://doi.org/10.1061/JTEPBS.0000591.
Kwon, D., S. E. S. Oh, S. Choi, and B. H. Kim. 2022. “Viability of compact cities in the post-COVID-19 era: Subway ridership variations in Seoul Korea.” Ann. Reg. Sci. 71 (1): 175–203. https://doi.org/10.1007/s00168-022-01119-9.
Labonté-LeMoyne, É, S.-L. Chen, C. K. Coursaris, S. Sénécal, and P.-M. Léger. 2020. “The unintended consequences of COVID-19 mitigation measures on mass transit and car use.” Sustainability 12 (23): 9892. https://doi.org/10.3390/su12239892.
Li, P., X. Chen, C. Ma, C. Zhu, and W. Lu. 2022. “Risk assessment of COVID-19 infection for subway commuters integrating dynamic changes in passenger numbers.” Environ. Sci. Pollut. Res. 29: 74715-74724. https://doi.org/10.1007/s11356-022-20920-9.
Li, J., Y. Long, and A. Dang. 2018. “Live-Work-Play centers of Chinese cities: Identification and temporal evolution with emerging data.” Comput. Environ. Urban Syst. 71: 58–66. https://doi.org/10.1016/j.compenvurbsys.2018.04.002.
Li, P., Q. Yang, and W. Lu. 2024. “Nonlinear relationship of multi-source land use features with temporal travel distances at subway station level: Empirical study from Xi’an city.” Land 13 (7): 1021. https://doi.org/10.3390/land13071021.
Liu, J., Q. Cao, and M. Pei. 2022a. “Impact of COVID-19 on adolescent travel behavior.” J. Transp. Health 24: 101326. https://doi.org/10.1016/j.jth.2021.101326.
Liu, J., and L. Xiao. 2023. “Non-linear relationships between built environment and commuting duration of migrants and locals.” J. Transp. Geogr. 106: 103517. https://doi.org/10.1016/j.jtrangeo.2022.103517.
Liu, Y., T. Pei, C. Song, J. Chen, X. Chen, Q. Huang, X. Wang, H. Shu, X. Wang, and S. Guo. 2021. “How did human dwelling and working intensity change over different stages of COVID-19 in Beijing?” Sustainable Cities Soc. 74: 103206. https://doi.org/10.1016/j.scs.2021.103206.
Liu, Z., X. C. Wang, J. Dai, X. Li, and R. Li. 2022b. “Impacts of COVID-19 pandemic on travel behavior in large cities of China: Investigation on the lockdown and reopening phases.” J. Transp. Eng. Part A. Syst. 148 (2): 05021011. https://doi.org/10.1061/JTEPBS.0000630.
Long, Y., and J.-C. Thill. 2015. “Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing.” Comput. Environ. Urban Syst. 53: 19–35. https://doi.org/10.1016/j.compenvurbsys.2015.02.005.
Lu, W., Y. Zhang, C. Ma, B. Zhou and T. Wang. 2022. “Measuring the relationship between influence factor and urban rail transit passenger flow: Correlation or causality?” J. Urban Plann. Develop. 148 (3): 05022025. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000870.
Luan, S., Q. Yang, Z. Jiang, and W. Wang. 2021. “Exploring the impact of COVID-19 on individual's travel mode choice in China.” Transp. Policy 106: 271–280. https://doi.org/10.1016/j.tranpol.2021.04.011.
Marra, A. D., L. Sun, and F. Corman. 2022. “The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area.” Transp. Policy 116: 258–268. https://doi.org/10.1016/j.tranpol.2021.12.009.
Osorio, J., Y. Liu, and Y. Ouyang. 2022. “Executive orders or public fear: What caused transit ridership to drop in Chicago during COVID-19?” Transp. Res. Part D Transp. Environ. 105: 103226. https://doi.org/10.1016/j.trd.2022.103226.
Park, J. 2020. “Changes in subway ridership in response to COVID-19 in Seoul, South Korea: Implications for social distancing.” Cureus 12 (4): e7668.
Parker, M. E., M. Li, M. A. Bouzaghrane, H. Obeid, D. Hayes, K. T. Frick, D. A. Rodríguez, R. Sengupta, J. Walker, and D. G. Chatman. 2021. “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.” Transp. Policy 111: 53–62. https://doi.org/10.1016/j.tranpol.2021.07.005.
Sung, H., K. Choi, S. Lee, and S. Cheon. 2014. “Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership.” J. Transp. Geogr. 36: 134–140. https://doi.org/10.1016/j.jtrangeo.2014.03.013.
Tao, T., and J. Cao. 2023. “Exploring nonlinear and collective influences of regional and local built environment characteristics on travel distances by mode.” J. Transp. Geogr. 109: 103599. https://doi.org/10.1016/j.jtrangeo.2023.103599.
Teixeira, J. F., and M. Lopes. 2020. “The link between bike sharing and subway use during the COVID-19 pandemic: The case-study of New York's Citi Bike.” Transp. Res. Interdiscip. Perspect. 6: 100166. https://doi.org/10.1016/j.trip.2020.100166.
Van Wee, B., and F. Witlox. 2021. “COVID-19 and its long-term effects on activity participation and travel behaviour: A multiperspective view.” J. Transp. Geogr. 95: 103144. https://doi.org/10.1016/j.jtrangeo.2021.103144.
Vichiensan, V., Y. Hayashi, and S. Kamnerdsap. 2021. “COVID-19 Countermeasures and passengers’ confidence of urban rail travel in Bangkok.” Sustainability 13 (16): 9377. https://doi.org/10.3390/su13169377.
Wang, Z., Y. Hu, P. Zhu, Y. Qin, and L. Jia. 2018. “Ring aggregation pattern of metro passenger trips: A study using smart card data.” Physica A 491: 471–479. https://doi.org/10.1016/j.physa.2017.08.105.
Xiao, W., Y. D. Wei, and Y. Wu. 2022. “Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic.” Transp. Res. Part D Transp. Environ. 110: 103428. https://doi.org/10.1016/j.trd.2022.103428.
Xin-yue, X., K. Qing-xue, L. Jian-min, L. Jun, and S. Qi. 2023. “Analysis of spatio-temporal heterogeneity impact of built environment on rail transit passenger flow.” J. Transp. Syst. Eng. Inf. Technol. 23 (4): 194.
Yang, H., Z. Guo, G. Zhai, L. Yang, and J. Huo. 2022a. “Exploring the spatially heterogeneous effects of the built environment on bike-sharing usage during the COVID-19 pandemic.” J. Adv. Transp. 2022: 7772401.
Yang, H., Z. Zhao, C. Jiang, Y. Wen, and M. Muneeb Abid. 2022b. “Spatially varying relation between built environment and station-level subway passenger-distance.” J. Adv. Transp. 2022: 1–18.
Yang, L., B. Yu, Y. Liang, Y. Lu, and W. Li. 2023. “Time-varying and non-linear associations between metro ridership and the built environment.” Tunnelling Underground Space Technol. 132: 104931. https://doi.org/10.1016/j.tust.2022.104931.
Yu, L., Y. Cong, and K. Chen. 2020. “Determination of the peak hour ridership of metro stations in Xi’an, China using geographically-weighted regression.” Sustainability 12 (6): 2255. https://doi.org/10.3390/su12062255.
Zhang, J., Y. Hayashi, and L. D. Frank. 2021a. “COVID-19 and transport: Findings from a world-wide expert survey.” Transp. Policy 103: 68–85. https://doi.org/10.1016/j.tranpol.2021.01.011.
Zhang, L., J. Hong, A. Nasri, and Q. Shen. 2012. “How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities.” J. Transp. Land Use 5 (3): 40–52. https://doi.org/10.5198/jtlu.v5i3.266.
Zhang, N., W. Jia, P. Wang, C.-H. Dung, P. Zhao, K. Leung, B. Su, R. Cheng, and Y. Li. 2021b. “Changes in local travel behaviour before and during the COVID-19 pandemic in Hong Kong.” Cities 112: 103139. https://doi.org/10.1016/j.cities.2021.103139.
Zhou, M., D. Wang, and X. Guan. 2022. “Co-evolution of the built environment and travel behaviour in Shenzhen, China.” Transp. Res. Part D Transp. Environ. 107: 103291. https://doi.org/10.1016/j.trd.2022.103291.
Information & Authors
Information
Published In
Journal of Urban Planning and Development
Volume 151 • Issue 1 • March 2025
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 2, 2024
Accepted: Sep 17, 2024
Published online: Oct 28, 2024
Published in print: Mar 1, 2025
Discussion open until: Mar 28, 2025
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Peikun Li, Quantao Yang, Wenbo Lu, Shu Xi, Hao Wang, An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environment, Land, 10.3390/land13111887, 13, 11, (1887), (2024).