Is Ride-Hailing Competing or Cooperating with Subway? A Survey in Chinese Cities

Abstract

Ride-hailing services, characterized by convenience, flexibility, and on-demand availability, have substantially reshaped urban mobility patterns, posing uncertain impacts on subway ridership. To clarify the competitive and cooperative dynamics between two modes, a latent class choice model (LCCM) is employed using revealed preference survey data (N = 2061) collected from Beijing, Shanghai, and Shenzhen in China. Three distinct commuter segments are identified through latent class analysis, and results indicate that ride-hailing and subway predominantly exhibit a competitive relationship (46.34%). Moreover, user heterogeneity is evident across user groups. Competitive users ride more frequently and commute shorter distances, and carless users tend toward competitive relation, while cooperative behaviors are linked to income and metro accessibility. Interestingly, commonly used spatial proximity assumption in previous studies exerts few influences on empirical modal interactions. Designing user-targeted takeaways for different transportation participants, this work offers valuable insights for improving multimodal travel efficiency and sustainable urban mobility.

Keywords

Ride-hailing / Subway / Competition and cooperation relationship / Revealed preference / Latent class choice model / Commuting

References

[1] Land Transport Authority, Singapore (1990) Vehicle Quota System in Singapore. https://www.lta.gov.sg. Accessed 9 June 2025.

[2] Land Transport Authority, Singapore (2019) Land transport master plan 2040. https://www.lta.gov.sg/content/dam/ltagov/who_we_are/our_work/land_transport_master_plan_2040/pdf/LTA%20LTMP%202040%20eReport.pdf. Accessed 9 Jun 2025

[3] Beijing Municipal Commission of Planning and Natural Resources (2022) Beijing rail transit micro-center plan (2020–2035). http://ghzrzyw.beijing.gov.cn/. Accessed 9 Jun 2025

[4] Erhardt GD, Hoque JM, Goyal V, Berrebi S, Brakewood C, Watkins KE. Why has public transit ridership declined in the United States?. Transp Res A Policy Pract, 2022, 161: 68-87.

[5] Lu K, Zhang L, Li S, Chen Y, Wang J. Urban rail transit in China: progress report and analysis (2015–2023). Urban Rail Transit, 2025, 11(1): 1-27.

[6] Rayle L, Dai D, Chan N, Cervero R, Shaheen S. Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco. Transp Policy, 2016, 45: 168-178.

[7] Jin ST, Kong H, Wu RJ, Sui DZ. Ridesourcing, the sharing economy, and the future of cities. Cities, 2018, 76: 96-104.

[8] Shi X, Li Z, Xia E. The impact of ride-hailing and shared bikes on public transit: moderating effect of the legitimacy. Res Transp Econ, 2021, 85. ArticleID: 100870

[9] Tirachini A, Gomez-Lobo A. Does ride-hailing increase or decrease vehicle kilometers traveled (VKT)? A simulation approach for Santiago de Chile. Int J Sustain Transp, 2020, 14(3): 187-204.

[10] Schaller B. Can sharing a ride make for less traffic? Evidence from Uber and Lyft and implications for cities. Transp Policy, 2021, 102: 1-10.

[11] Henao A, Marshall WE. The impact of ride hailing on parking (and vice versa). J Transp Land Use, 2019, 12(1): 127-147.

[12] Wang J, Huang J, Yang H, Levinson D. Resilience and recovery of public transport use during COVID-19. npj Urban Sustain, 2022, 2. ArticleID: 18

[13] Hall JD, Palsson C, Price J. Is Uber a substitute or complement for public transit?. J Urban Econ, 2018, 108(5): 36-50.

[14] Scholl L, Bedoya-Maya F, Sabogal-Cardona O, Oviedo D (2021) Making the links between ride-hailing and public transit ridership: impacts in medium and large Colombian cities. IDB Working Paper Series No. IDB-WP-01237, Inter-American Development Bank

[15] Cats O, Kucharski R, Danda SR, Yap M. Beyond the dichotomy: how ride-hailing competes with and complements public transport. PLoS ONE, 2022, 17(1. ArticleID: e0262496

[16] Zhang Y, Zhang Y. Exploring the relationship between ridesharing and public transit use in the United States. Int J Environ Res Public Health, 2018, 15(8): 1763.

[17] Li W, Shalaby A, Habib KN. Exploring the correlation between ride-hailing and multimodal transit ridership in Toronto. Transp, 2022, 49(3): 765-789.

[18] Liao Y. Ride-sourcing compared to its public-transit alternative using big trip data. J Transp Geogr, 2021, 95. ArticleID: 103135

[19] Jiang S, Guan W, He Z, Yang L. Exploring the intermodal relationship between taxi and subway in Beijing, China. J Adv Transp, 2018, 2018. ArticleID: 3981845

[20] Henao A (2017) Impacts of ridesourcing—Lyft and Uber—on transportation including VMT, mode replacement, parking, and travel behavior. Doctoral dissertation, University of Colorado Denver

[21] Hampshire RC, Simek C, Fabusuyi T, Di X, Chen X (2017) Measuring the impact of an unanticipated disruption of Uber/Lyft in Austin, TX. SSRN Electron J. https://doi.org/10.2139/ssrn.2977969

[22] Wang Y, Moudon AV, Shen Q. How does ride-hailing influence individual mode choice? An examination using longitudinal trip data from the Seattle region. Transp Res Rec, 2022, 2676(3): 621-633.

[23] Wu X, MacKenzie D. Assessing the VMT effect of ridesourcing services in the US. Transp Res D Transp Environ, 2021, 94. ArticleID: 102816

[24] Wali B, Santi P, Ratti C. Modeling ride-hailing and carsharing adoption & use patterns: deciphering the substitutive and complementary impacts of built environment, transit accessibility, & active travel. Sustain Cities Soc, 2025, 120. ArticleID: 105931

[25] Kim SH, Mokhtarian PL. Finite mixture (or latent class) modeling in transportation: trends, usage, potential, and future directions. Transp Res B Methodol, 2023, 172: 134-173.

[26] Lee Y, Chen GY-H, Circella G, Mokhtarian PL. Substitution or complementarity? A latent-class cluster analysis of ridehailing impacts on the use of other travel modes in three southern U.S. cities. Transp Res D Transp Environ, 2022, 104. ArticleID: 103167

[27] Loa P, Ong F, Habib KN, Alex FJ, Mashrur SM. Identifying profiles of ride-sourcing users in the Metro Vancouver Region for a better understanding of ride-sourcing behaviour. Travel Behav Soc, 2023, 33. ArticleID: 100637

[28] Iogansen X, Lee Y, Young M, Compostella J, Circella G, Jenn A. Ridehailing use, travel patterns and multimodality: a latent-class cluster analysis of one-week GPS-based travel diaries in California. Travel Behav Soc, 2025, 38. ArticleID: 100855

[29] Babar Y, Burtch G. Examining the heterogeneous impact of ride-hailing services on public transit use. Inf Syst Res, 2020, 31(3): 820-834.

[30] Kong H, Zhang X, Zhao J. How does ridesourcing substitute for public transit? A geospatial perspective in Chengdu, China. J Transp Geogr, 2020, 86. ArticleID: 102769

[31] Qiao H, Yeh AG-O. Is ride-hailing competing or complementing public transport? A perspective from affordability. Transp Res D Transp Environ, 2023, 114. ArticleID: 103533

[32] Dias FF, Lavieri PS, Garikapati VM, Astroza S, Pendyala RM, Bhat CR. A behavioral choice model of the use of car-sharing and ride-sourcing services. Transportation, 2017, 44(6): 1307-1323.

[33] Alemi F, Circella G, Handy S, Mokhtarian P. What influences travelers to use Uber? Exploring the factors affecting the adoption of on-demand ride services in California. Travel Behav Soc, 2018, 13: 88-104.

[34] Young M, Farber S. The who, why, and when of Uber and other ride-hailing trips: an examination of a large sample household travel survey. Transp Res A Policy Pract, 2019, 119: 383-392.

[35] Ge W, Shao D, Xue M, Zhu H, Cheng J. Urban taxi ridership analysis in the emerging metropolis: case study in Shanghai. Transp Res Procedia, 2017, 25: 4916-4927.

[36] Smith A (2016) Shared, collaborative and on demand: the new digital economy. Pew Research Center

[37] Clewlow R, Mishra GS (2017) Disruptive transportation: the adoption, utilization, and impacts of ride-hailing in the United States. Research Report UCD-ITS-RR-17-07, Institute of Transportation Studies, University of California, Davis

[38] Yu H, Peng Z-R. Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression. J Transp Geogr, 2019, 75: 147-163.

[39] Wang S, Noland RB. Variation in ride-hailing trips in Chengdu, China. Transp Res D Transp Environ, 2021, 90. ArticleID: 102596

[40] Liu F, Gao F, Yang L, Han C, Hao W, Tang J. Exploring the spatially heterogeneous effect of the built environment on ride-hailing travel demand: a geographically weighted quantile regression model. Travel Behav Soc, 2022, 29: 22-33.

[41] Bai T, Li X, Sun Z. Effects of cost adjustment on travel mode choice: analysis and comparison of different logit models. Transp Res Procedia, 2017, 25: 2649-2659.

[42] Shen J. Latent class model or mixed logit model? A comparison by transport mode choice data. Appl Econ, 2009, 41(22): 2915-2924.

[43] Lee K, Jin Q, Animesh A, Ramaprasad J (Forthcoming) On the impact of ride-hailing services on the transportation mode choices of drivers, riders, and walkers: an empirical study of Uber entry. MIS Quarterly

[44] BigData-Research (BDC) (2017) 2017 Shang Bannian Zhongguo Wangyue Che APP Chanpin Jiance Baogao (Quanwen). Bida Wang. http://www.bigdata-research.cn/. Accessed 16 October 2017.

[45] Beijing Municipal Bureau of Statistics (ed) (2018) Beijing statistical yearbook 2018. China Statistics Press, Beijing

[46] Shanghai Municipal Bureau of Statistics (ed) (2018) Shanghai statistical yearbook 2018. China Statistics Press, Shanghai

[47] Shenzhen Municipal Bureau of Statistics (2018) Shenzhen statistical yearbook 2018. http://tjj.sz.gov.cn/xxgk/zfxxgkml/tjsj/tjnj/.

[48] Dong Y, Cao P, Yao X, Zhang J, Tian D, Li J, Zhao J (2023) Analysis report on green travel and its carbon emissions in Chinese cities

[49] Lu Y, Sun G, Sarkar C, Gou Z, Xiao Y. Commuting mode choice in a high-density city: do land-use density and diversity matter in Hong Kong?. Int J Environ Res Public Health, 2018, 15(5): 920.

[50] Li W, Kamargianni M. An integrated choice and latent variable model to explore the influence of attitudinal and perceptual factors on shared mobility choices and their value of time estimation. Transp Sci, 2020, 54(1): 62-83.

[51] Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model, 2007, 14(4): 535-569.

[52] Weller BE, Bowen NK, Faubert SJ. Latent class analysis: a guide to best practice. J Black Psychol, 2020, 46(4): 287-311.

[53] Lü Y, He L, Sun H, Xu G. Study on the substitution relationship between ride-hailing and public transportation and its emission reduction potential. J Transp Syst Eng Inf Technol, 2023, 23(4): 11-23

[54] Bhat CR, Singh SK. A comprehensive daily activity-travel generation model system for workers. Transp Res A Policy Pract, 2000, 34(1): 1-22.

[55] Misra R, Bhat CR. Activity travel patterns of non-workers in the San Francisco Bay Area: exploratory analysis. Transp Res Rec, 2000, 1718: 43-51.

[56] Wang J, Hu Y. Constructing the commuting backbone network dataset for the United States. Sci Data, 2025, 12. ArticleID: 1267

[57] Dawes M (2016) Perspectives on the ridesourcing revolution: Surveying individual attitudes toward Uber and Lyft to inform urban transportation policymaking. Master’s thesis, Massachusetts Institute of Technology. https://hdl.handle.net/1721.1/104994.

[58] Shi K, Shao R, De Vos J, Cheng L, Witlox F. The influence of ride-hailing on travel frequency and mode choice. Transp Res D Transp Environ, 2021, 101. ArticleID: 103125

[59] Diao M, Song K, Shi S, Zhu Y, Liu B. The environmental benefits of dockless bike sharing systems for commuting trips. Transp Res D Transp Environ, 2023, 124. ArticleID: 103959

[60] Chuxing D (2025) Commuter bus – Didi official website. https://www.didiglobal.com/travel-service/bus. Accessed 7 Jul 2025

[61] Guo R, Liu X, Sun Y, Yan X, Guan W, Azadeh SS (2025) From ride-hailing to high-capacity ride-sharing: a user-centric shared mobility service design. Transportmet A Transp Sci

[62] Basunia A, Muttaqi A, Bhuiyan MRH, Badhon FA. Transportation mobility during COVID-19: a systematic review and bibliometric analysis. Digit Transp Saf, 2024, 3(3): 169-183.

[63] Ecke L, Magdolen M, Chlond B, Vortisch P. How the COVID-19 pandemic changes daily commuting routines—Insights from the German Mobility Panel. Case Stud Transp Policy, 2022, 10(4): 2175-2182.

[64] Gao Y, Zhao P. Tracing long-term commute mode choice shifts in Beijing: four years after the COVID‑19 pandemic. Hum Soc Sci Commun, 2024, 11. ArticleID: 1566

Funding

Fundamental Research Funds for the Central Universities(2024JBZX013)

RIGHTS & PERMISSIONS

The Author(s)