Impact Evaluation of COVID-19 on Transit Ridership: A Case Study of the Beijing Subway

Abstract

A comprehensive understanding of the multifaceted ramifications of the coronavirus disease 2019 (COVID-19) on transit ridership is imperative for the optimization of judicious traffic management policies. The intricate influences of this pandemic exhibit a high degree of complexity, dynamically evolving across spatial and temporal dimensions. At present, a nuanced understanding remains elusive regarding whether disparate influencing factors govern inbound and outbound passenger flows. This study propels the discourse forward by introducing a methodological synthesis that integrates time series anomaly detection, impact inference, and spatiotemporal analysis. This amalgamation establishes an analytical framework instrumental in elucidating the spatiotemporal heterogeneity intrinsic to individual impact events, grounded in extensive time series data. The resulting framework facilitates a nuanced delineation, affording a more precise extraction of the COVID-19 impact on subway ridership. Empirical findings derived from the daily trip data of the Beijing subway in 2020 substantiate the existence of conspicuous spatiotemporal variability in the determinants influencing relative shifts in inbound and outbound ridership. Notably, stations situated in high-risk areas manifest a conspicuous absence of correlation with outbound trips, exhibiting a discernibly negative impact solely on inbound trips. Conversely, stations servicing residential and enterprise locales demonstrate resilience, evincing an absence of significant perturbation induced by the outbreak.

Keywords

Urban rail transit / COVID-19 / Temporal anomaly detection in time series / Ridership relative impact / Geographically and temporally weighted regression model

References

[1] Zhu N, Zhang DY, Wang WL A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med, 2020

[2] Abdullah M, Ali N, Hussain SA Measuring changes in travel behavior pattern due to COVID-19 in a developing country: a case study of Pakistan. Transp Policy, 2021, 108: 21-33

[3] Liu ZY, Wang XK, Dai JC Impacts of COVID-19 pandemic on travel behavior in large cities of China: investigation on the lockdown and reopening phases. J Transp Eng Part Syst, 2022, 148: 05021011

[4] Liu ZX, Stern R. Quantifying the traffic impacts of the COVID-19 shutdown. J Transp Eng Part Syst, 2021, 147: 04021014

[5] Zhang YC, Fricker JD. Quantifying the impact of COVID-19 on non-motorized transportation: a Bayesian structural time series model. Transp Policy, 2021, 103: 11-20

[6] Jiang S, Cai C. Unraveling the dynamic impacts of COVID-19 on metro ridership: an empirical analysis of Beijing and Shanghai, China. Transp Policy, 2022, 127: 158-170

[7] Nasri A, Zhang L (2015) Assessing the impact of metropolitan-level, county-level, and local-level built environment on travel behavior: evidence from 19 U.S. urban areas. J Urban Plan Dev 141:04014031. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000226

[8] Sohn K, Shim H. Factors generating boardings at Metro stations in the Seoul metropolitan area. Cities, 2010, 27: 358-368

[9] Chen E, Ye Z, Wang C, Zhang W. Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data. Cities, 2019, 95: 102359

[10] Li S, Lyu D, Liu X The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou. Cities, 2020, 99: 102580

[11] Yin Y, Li D, Zhang S, Wu L. How does railway respond to the spread of COVID-19? Countermeasure analysis and evaluation around the world. Urban Rail Transit, 2021, 7: 29-57

[12] Zhang J, Zhang R, Ding H Effects of transport-related COVID-19 policy measures: a case study of six developed countries. Transp Policy, 2021, 110: 37-57

[13] Hu S, Chen P. Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership. Transp Res Part Transp Environ, 2021, 90: 102654

[14] Fang H, Wang L, Yang Y. Human mobility restrictions and the spread of the novel coronavirus (2019-nCoV) in China. J Public Econ, 2020, 191: 104272

[15] Wang J, Kaza N, McDonald NC, Khanal K. Socio-economic disparities in activity-travel behavior adaptation during the COVID-19 pandemic in North Carolina. Transp Policy, 2022, 125: 70-78

[16] Brough R, Freedman M, Phillips DC. Understanding socioeconomic disparities in travel behavior during the COVID-19 pandemic. J Reg Sci, 2021, 61: 753-774

[17] Chakraborty A, Mishra S. Land use and transit ridership connections: implications for state-level planning agencies. Land Use Policy, 2013, 30: 458-469

[18] Chen C, Chen J, Barry J. Diurnal pattern of transit ridership: a case study of the New York City subway system. J Transp Geogr, 2009, 17: 176-186

[19] Jun M-J, Choi K, Jeong J-E Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul. J Transp Geogr, 2015, 48: 30-40

[20] Zhu Y, Chen F, Wang Z, Deng J. Spatio-temporal analysis of rail station ridership determinants in the built environment. Transportation, 2019, 46: 2269-2289

[21] Shen L, Long Y, Tian L The impact of built environment on the commuting distance of middle/low-income tenant workers in mega cities based on nonlinear analysis in machine learning. Urban Rail Transit, 2023, 9: 294-309

[22] Xiao W, Wei YD, Wu Y. Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic. Transp Res Part Transp Environ, 2022, 110: 103428

[23] Yang H, Guo Z, Zhai G Exploring the spatially heterogeneous effects of the built environment on bike-sharing usage during the COVID-19 pandemic. J Adv Transp, 2022, 2022: e7772401

[24] Zhou M, Ma H, Wu J, Zhou J. Metro travel and perceived COVID-19 infection risks: a case study of Hong Kong. Cities, 2023, 137: 104307

[25] Wu J, Liao H. Weather, travel mode choice, and impacts on subway ridership in Beijing. Transp Res Part Policy Pract, 2020, 135: 264-279

[26] Singhal A, Kamga C, Yazici A. Impact of weather on urban transit ridership. Transp Res Part Policy Pract, 2014, 69: 379-391

[27] Li J, Li X, Chen D, Godding L. Assessment of metro ridership fluctuation caused by weather conditions in Asian context: using archived weather and ridership data in Nanjing. J Transp Geogr, 2018, 66: 356-368

[28] Wu P, Xu L, Zhong L Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations. J Transp Geogr, 2022, 104: 103415

[29] Lin P, Weng J, Brands DK Analysing the relationship between weather, built environment, and public transport ridership. IET Intell Transp Syst, 2020, 14: 1946-1954

[30] Shi G, Luo L. Prediction and impact analysis of passenger flow in urban rail transit in the postpandemic era. J Adv Transp, 2023, 2023: e3448864

[31] Wang H, Noland RB. Bikeshare and subway ridership changes during the COVID-19 pandemic in New York City. Transp Policy, 2021, 106: 262-270

[32] Weng J, Tu Q, Yuan R Modeling mode choice behaviors for public transport commuters in Beijing. J Urban Plan Dev, 2018, 144: 05018013

[33] Zhang Z, Fujii H, Managi S. How does commuting behavior change due to incentives? An empirical study of the Beijing Subway System. Transp Res Part F Traffic Psychol Behav, 2014, 24: 17-26

[34] Zhao P, Zhang Y. The effects of metro fare increase on transport equity: new evidence from Beijing. Transp Policy, 2019, 74: 73-83

[35] Dai J, Liu Z, Li R. Improving the subway attraction for the post-COVID-19 era: the role of fare-free public transport policy. Transp Policy, 2021, 103: 21-30

[36] Cardozo OD, García-Palomares JC, Gutiérrez J. Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Appl Geogr, 2012, 34: 548-558

[37] Zhao J, Deng W, Song Y, Zhu Y. What influences Metro station ridership in China? Insights from Nanjing. Cities, 2013, 35: 114-124

[38] Stewart Fotheringham A, Charlton M, Brunsdon C. The geography of parameter space: an investigation of spatial non-stationarity. Int J Geogr Inf Syst, 1996, 10: 605-627

[39] Gan Z, Feng T, Yang M Analysis of metro station ridership considering spatial heterogeneity. Chin Geogr Sci, 2019, 29: 1065-1077

[40] Tu W, Cao R, Yue Y Spatial variations in urban public ridership derived from GPS trajectories and smart card data. J Transp Geogr, 2018, 69: 45-57

[41] Li S, Lyu D, Huang G Spatially varying impacts of built environment factors on rail transit ridership at station level: a case study in Guangzhou. China. J Transp Geogr, 2020, 82: 102631

[42] Huang B, Wu B, Barry M. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int J Geogr Inf Sci, 2010, 24: 383-401

[43] Ma X, Zhang J, Ding C, Wang Y. A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Comput Environ Urban Syst, 2018, 70: 113-124

[44] Li X, Xing G, Qian X Subway station accessibility and its impacts on the spatial and temporal variations of its outbound ridership. J Transp Eng Part Syst, 2022, 148: 04022106

[45] Fraszczyk A, Weerawat W, Kirawanich P. Commuters’ willingness to shift to metro: a case study of Salaya, Thailand. Urban Rail Transit, 2019, 5: 240-253

[46] Kieu LM, Bhaskar A, Chung E. Passenger segmentation using smart card data. IEEE Trans Intell Transp Syst, 2015, 16: 1537-1548

[47] Pelletier M-P, Trépanier M, Morency C. Smart card data use in public transit: a literature review. Transp Res Part C Emerg Technol, 2011, 19: 557-568

[48] Sung H, Choi K, Lee S, Cheon S. Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership. J Transp Geogr, 2014, 36: 134-140

[49] Li X, Xiao Q, Zhu Y, Yang Y. Influence of TOD modes on passenger travel behavior in urban rail transit systems. Urban Rail Transit, 2022, 8: 175-183

[50] Bian Z, Zuo F, Gao J Time lag effects of COVID-19 policies on transportation systems: a comparative study of New York City and Seattle. Transp Res Part Policy Pract, 2021, 145: 269-283

[51] Erdman C, Emerson JW. A fast Bayesian change point analysis for the segmentation of microarray data. Bioinformatics, 2008, 24: 2143-2148

[52] Fearnhead P. Exact and efficient Bayesian inference for multiple changepoint problems. Stat Comput, 2006, 16: 203-213

[53] Erdman C, Emerson JW (2008) bcp: an R package for performing a Bayesian analysis of change point problems. J Stat Softw 23:1–13. https://doi.org/10.18637/jss.v023.i03

[54] Osorio J, Liu Y, Ouyang Y. Executive orders or public fear: what caused transit ridership to drop in Chicago during COVID-19?. Transp Res Part Transp Environ, 2022, 105: 103226

[55] Sung H. Causal impacts of the COVID-19 pandemic on daily ridership of public bicycle sharing in Seoul. Sustain Cities Soc, 2023, 89: 104344

[56] Xi H, Nelson JD, Hensher DA Evaluating travel behavior resilience across urban and rural areas during the COVID-19 pandemic: contributions of vaccination and epidemiological indicators. Transp Res Part Policy Pract, 2024, 180: 103980

[57] Yabe T, Zhang Y, Ukkusuri SV. Quantifying the economic impact of disasters on businesses using human mobility data: a Bayesian causal inference approach. EPJ Data Sci, 2020, 9: 1-20

[58] Brodersen KH, Gallusser F, Koehler J Inferring causal impact using Bayesian structural time-series models. Ann Appl Stat, 2015, 9: 247-274

[59] Scott SL, Varian HR. Predicting the present with Bayesian structural time series. Int J Math Model Numer Optim, 2014, 5: 4-23

[60] Fotheringham AS, Crespo R, Yao J. Geographical and temporal weighted regression (GTWR). Geogr Anal, 2015, 47: 431-452

[61] Chu HJ, Kong SJ, Chang CH. Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression. Int J Appl Earth Obs Geoinf, 2018, 65: 1-11

[62] Zhang WB, Ge Y, Liu MX Risk assessment of the step-by-step return-to-work policy in Beijing following the COVID-19 epidemic peak. Stoch Environ Res Risk Assess, 2021, 35: 481-498

[63] Zhou XZ, Ji YJ, Yuan YD Spatiotemporal characteristics analysis of commuting by shared electric bike: a case study of Ningbo. China. J Clean Prod, 2022, 362: 132337

[64] Wang C, Wang XJ, Pan RB, Yan YS. Influence of built environment on subway trip origin and destination: insights based on mobile positioning data. Transp Res Rec J Transp Res Board, 2022, 2676: 693-710

[65] Wang YH, Correia GH de A, de Romph E, Timmermans HJP (2017) Using metro smart card data to model location choice of after-work activities: An application to Shanghai. J Transp Geogr 63:40–47. https://doi.org/10.1016/j.jtrangeo.2017.06.010

Funding

Fundamental Research Funds for the Central Universities(No. 2022JBZY039)