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Measuring spatial equity between metro accessibility and public service demand in Shanghai using a data-driven framework
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  • Published: 13 February 2026

Measuring spatial equity between metro accessibility and public service demand in Shanghai using a data-driven framework

  • Pei Jiang1,3,
  • Yawen Liu3,4,
  • Xuewen Shi3,
  • Hui Zhuo3,
  • Yuntao Liu3,
  • Min Erin Fu5,
  • Siqi Lin1,2 &
  • …
  • Shijun Cheng6 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Complex networks
  • Geography
  • Science, technology and society

Abstract

Urban metro systems play a central role in shaping mobility opportunities and access to essential public services, yet these benefits are not always shared equitably across cities. This study evaluates equitable mobility in Shanghai by examining how metro-based accessibility to public services aligns with actual passenger demand. We develop a multidimensional accessibility index for each station based on travel time to diverse public services and integrate it with passenger flow data to construct a local alignment index. A Lorenz curve and Gini coefficient capture system-wide distributional patterns. The analysis shows clear and persistent inequities: central stations maintain strong alignment between accessibility and demand, while many peripheral stations consistently face high demand but limited access. The system-wide Gini coefficient of 0.348 reflects a notable imbalance in the distribution of accessibility benefits. These results highlight inequitable spatial pattern embedded in the metro network’s spatial design. The study introduces a replicable, behaviour-sensitive framework that links actual mobility patterns with public service accessibility. It also provides actionable evidence for spatial-equity-oriented metro planning, emphasizing the need for targeted improvements in underserved areas and stronger coordination between land-use and transit development. Together, these contributions support more inclusive and equitable urban mobility planning.

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Data availability

The datasets generated and/or analysed during the current study are not publicly available due to commercial confidentiality agreements with the data provider but are available from the corresponding author on reasonable request.

References

  1. Tosun, H. B. Engineering urban mobility through a strategic framework for tram route design. Sci. Rep. 15, 41123. https://doi.org/10.1038/s41598-025-24987-5 (2025).

    Google Scholar 

  2. Tomasiello, D. B. & Giannotti, M. Unfolding time, race and class inequalities to access leisure. Environ. Plann. B Urban Anal. City Sci. 50, 927–941. https://doi.org/10.1177/23998083221111405 (2023).

    Google Scholar 

  3. Bittencourt, T. A. & Giannotti, M. Evaluating the accessibility and availability of public services to reduce inequalities in everyday mobility. Transp. Res. Part A Policy Practice 177, 103833. https://doi.org/10.1016/j.tra.2023.103833 (2023).

    Google Scholar 

  4. Tang, T. et al. Deciphering the pulse of the city: An exploration of the natural features of metro passenger flow using XAI. Comput. Ind. Eng. 204, 111097. https://doi.org/10.1016/j.cie.2025.111097 (2025).

    Google Scholar 

  5. Tang, T. et al. A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China. Transp. Res. Part A Policy Practice 183, 104049. https://doi.org/10.1016/j.tra.2024.104049 (2024).

    Google Scholar 

  6. Zhang, S., Sadagopan, M. & Qin, X. Evaluating the usefulness of VGI for citizen co-producing city services from citizen perspective: A case study of crowdsourcing pedestrian navigation. Multimodal Transp. 4, 100223. https://doi.org/10.1016/j.multra.2025.100223 (2025).

    Google Scholar 

  7. Farrington, J. & Farrington, C. Rural accessibility, social inclusion and social justice: Towards conceptualisation. J. Transp. Geogr. 13, 1–12. https://doi.org/10.1016/j.jtrangeo.2004.10.002 (2005).

    Google Scholar 

  8. Allen, J. & Farber, S. Planning transport for social inclusion: An accessibility-activity participation approach. Transp. Res. Part D Transp. Environ. 78, 102212. https://doi.org/10.1016/j.trd.2019.102212 (2020).

    Google Scholar 

  9. Wang, G. et al. Research on accessibility of port collection and distribution system from the perspective of carbon emissions. Front. Mar. Sci. https://doi.org/10.3389/fmars.2023.1330717 (2023).

    Google Scholar 

  10. Tang, T. et al. Origin-destination matrix prediction in public transport networks: Incorporating heterogeneous direct and transfer trips. IEEE Trans. Intell. Transp. Syst. https://doi.org/10.1109/TITS.2024.3447611 (2024).

    Google Scholar 

  11. Alamri, S., Adhinugraha, K., Allheeib, N. & Taniar, D. GIS analysis of adequate accessibility to public transportation in metropolitan areas. ISPRS Int. J. Geo-Inf. 12, 180. https://doi.org/10.3390/ijgi12050180 (2023).

    Google Scholar 

  12. He, N. et al. Evaluation of highway debris flow hazard based on geomorphic evolution theory coupled with material response rate. Sci. Rep. 14, 13533. https://doi.org/10.1038/s41598-024-64279-y (2024).

    Google Scholar 

  13. Malik, A. Z. et al. Enhancing smart city mobility through real time explainable AI in autonomous vehicles. Sci. Rep. 15, 42118. https://doi.org/10.1038/s41598-025-25993-3 (2025).

    Google Scholar 

  14. Zhao, J., Deng, W., Song, Y. & Zhu, Y. What influences Metro station ridership in China? Insights from Nanjing. Cities 35, 114–124. https://doi.org/10.1016/j.cities.2013.07.002 (2013).

    Google Scholar 

  15. Yang, Y., Heppenstall, A., Turner, A. & Comber, A. Who, where, why and when? Using smart card and social media data to understand urban mobility. ISPRS Int. J. Geo-Inf. 8, 271 (2019).

    Google Scholar 

  16. Tang, T. et al. Why metro passengers change travel behavior: Individual-level insights from interpretable machine learning. Cities 167, 106352. https://doi.org/10.1016/j.cities.2025.106352 (2025b).

    Google Scholar 

  17. Chang, W., Zhang, Y. & Fu, X. Assessment of green space accessibility incorporating sentiment analysis: An improved 2SFCA method. J. Geo-Inf. Sci. 26, 2243–2253. https://doi.org/10.12082/dqxxkx.2024.240096 (2024)

  18. Askari, S., Merikhipour, M., Rahimi, E., Peiravian, F. & (Kouros) Mohammadian, A. Uncovering individual-level determinants of shared e-scooting travel frequency. Multimodal Transp. 4, 100228 (2025). https://doi.org/10.1016/j.multra.2025.100228.

  19. Rui, Y., Shi, J., Mao, C., Liao, P. & Li, S. Mining asymmetric traffic behavior at signalized intersections using a cellular automaton framework. Symmetry 17, 1328. https://doi.org/10.3390/sym17081328 (2025).

    Google Scholar 

  20. Tang, T., Zhong, S., Chen, Y. & Luo, L. Accounting for taxi service conditions in estimating route travel time from floating car data using Markov chain model. Multimodal Transp. 3, 100172. https://doi.org/10.1016/j.multra.2024.100172 (2024).

    Google Scholar 

  21. Coombs, N. C., Meriwether, W. E., Caringi, J. & Newcomer, S. R. Barriers to healthcare access among U.S. adults with mental health challenges: A population-based study. SSM - Population Health 15, 100847 (2021), https://doi.org/10.1016/j.ssmph.2021.100847.

  22. Xiong, J., Xu, Z., Li, L. & Liu, X. The effect of cross-boundary supply on the accessibility of public services in urban governance: An example of Shanghai. Sustainability 14, 12771. https://doi.org/10.3390/su141912771 (2022).

    Google Scholar 

  23. Pan, J., Deng, Y., Yang, Y. & Zhang, Y. Location-allocation modelling for rational health planning: Applying a two-step optimization approach to evaluate the spatial accessibility improvement of newly added tertiary hospitals in a metropolitan city of China. Soc. Sci. Med. 338, 116296. https://doi.org/10.1016/j.socscimed.2023.116296 (2023).

    Google Scholar 

  24. Yoshida, N. & Deichmann, U. Measurement of accessibility and its applications. J. Infrastruct. Dev. 1, 1–16. https://doi.org/10.1177/097493060900100102 (2009).

    Google Scholar 

  25. Liu, J., Zhong, S., Huang, Y. & Wang, Z. How does the preference heterogeneity affect the elderly’s evaluation of bus accessibility? Evidence from Guangzhou, China. J. Transp. Health 22, 101094. https://doi.org/10.1016/j.jth.2021.101094 (2021).

    Google Scholar 

  26. Pei, A., Xiao, F., Yu, S. & Li, L. Efficiency in the evolution of metro networks. Sci. Rep. 12, 8326. https://doi.org/10.1038/s41598-022-12053-3 (2022).

    Google Scholar 

  27. Liu, S. & Zhu, X. Accessibility analyst: An integrated GIS tool for accessibility analysis in urban transportation planning. Environ. Plann. B Plann. Des. 31, 105–124. https://doi.org/10.1068/b305 (2004).

    Google Scholar 

  28. Evidence from Atlanta. Helling, A. Changing intra-metropolitan accessibility in the U.S. Progress in Planning 49, iii–107. https://doi.org/10.1016/S0305-9006(97)00032-9 (1998).

    Google Scholar 

  29. Liu, S. & Zhu, X. An integrated GIS approach to accessibility analysis. Trans. GIS 8, 45–62. https://doi.org/10.1111/j.1467-9671.2004.00167.x (2004).

    Google Scholar 

  30. Zhou, Y., Fu, X., Xie, Y., Tang, T. & Lü, G. Non-linear and temporally dynamic effects of built environment and trip attributes on metro competitiveness. J. Transp. Geogr. 129, 104443. https://doi.org/10.1016/j.jtrangeo.2025.104443 (2025).

    Google Scholar 

  31. Zhou, Y., Fu, X., Tang, T., Vo, K. D. & Hato, E. Assessing resilience of transit networks: An activity-based space-time accessibility analysis. Sustain. Cities Soc. 130, 106676. https://doi.org/10.1016/j.scs.2025.106676 (2025).

    Google Scholar 

  32. Jiang, Y., Guo, D., Li, Z. & Hodgson, M. E. A novel big data approach to measure and visualize urban accessibility. Comput. Urban Sci. 1, 10. https://doi.org/10.1007/s43762-021-00010-1 (2021).

    Google Scholar 

  33. Pan, Z. G., Lan, G. W., Fan, D. L., Du, Y. L. & Zeng, Y. Analysis of accessibility of urban roads based on space syntax and distance measurement. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10, 159–165 (2020). https://doi.org/10.5194/isprs-archives-XLII-3-W10-159-2020.

  34. Rajendran, P., Bindhu, B. K. & Sanjay Kumar, V. S. Public transport accessibility index for Thiruvananthapuram urban area. J. Mech. Civil Eng. 7, 61–66 (2013).

    Google Scholar 

  35. Guo, Y., Chen, Z., Stuart, A., Li, X. & Zhang, Y. A systematic overview of transportation equity in terms of accessibility, traffic emissions, and safety outcomes: From conventional to emerging technologies. Transp. Res. Interdiscipl. Perspect. 4, 100091. https://doi.org/10.1016/j.trip.2020.100091 (2020).

    Google Scholar 

  36. Pereira, R. H. M., Schwanen, T. & Banister, D. Distributive justice and equity in transportation. Transp. Rev. 37, 170–191. https://doi.org/10.1080/01441647.2016.1257660 (2017).

    Google Scholar 

  37. Rowangould, D., Karner, A., Levine, K. & Alcorn, L. “I’d like accessibility analysis to help us shape the future’’: Transportation practitioners and accessibility measurement. Transp. Res. Record J. Transp. Res. Board 2678, 1536–1550. https://doi.org/10.1177/03611981241239653 (2024).

    Google Scholar 

  38. Best, K. L. et al. Housing, transportation and quality of life among people with mobility limitations: A critical review of relationships and issues related to access to home- and community-based services. Disabilities 2, 204–218. https://doi.org/10.3390/disabilities2020015 (2022).

    Google Scholar 

  39. Miller, E. J. Accessibility: Measurement and application in transportation planning. Transp. Rev. 38, 551–555. https://doi.org/10.1080/01441647.2018.1492778 (2018).

    Google Scholar 

  40. Sun, X., Liu, H., Liao, C., Nong, H. & Yang, P. Understanding recreational ecosystem service supply-demand mismatch and social groups’ preferences: Implications for urban-rural planning. Landsc. Urban Plann. 241, 104903. https://doi.org/10.1016/j.landurbplan.2023.104903 (2024).

    Google Scholar 

  41. Higgins, C. D. et al. Calculating place-based transit accessibility: Methods, tools and algorithmic dependenc. J. Transp. Land Use 15, 95–116 (2022).

    Google Scholar 

  42. Zhu, C., Zhou, Z., Ma, G. & Yin, L. Spatial differentiation of the impact of transport accessibility on the multidimensional poverty of rural households in karst mountain areas. Environ. Dev. Sustain. 24, 3863–3883. https://doi.org/10.1007/s10668-021-01591-x (2022).

    Google Scholar 

  43. chen, h., Li, L. & Qin, X. Research on the accessibility of rural transportation in the context of rural revitalization: A case study of Jiang’an. In Xiao, X. & Yao, J. (eds.) Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 22 (SPIE. 2024). https://doi.org/10.1117/12.3054498.

  44. Huang, H. et al. MTLMetro: A deep multi-task learning model for metro passenger demands prediction. IEEE Trans. Intell. Transp. Syst. 1, 16. https://doi.org/10.1109/TITS.2024.3373565 (2024).

    Google Scholar 

  45. Hansen, W. G. How accessibility shapes land use. J. Am. Inst. Plann. 25, 73–76. https://doi.org/10.1080/01944365908978307 (1959).

    Google Scholar 

  46. Shen, Q. Spatial technologies, accessibility, and the social construction of urban space. Comput. Environ. Urban Syst. 22, 447–464. https://doi.org/10.1016/S0198-9715(98)00039-8 (1998).

    Google Scholar 

  47. Papa, E. & Bertolini, L. Accessibility and transit-oriented development in European metropolitan areas. J. Transp. Geogr. 47, 70–83. https://doi.org/10.1016/j.jtrangeo.2015.07.003 (2015).

    Google Scholar 

  48. Van Thang, N. Data-driven insights into socio-economic disparities in urban transportation accessibility. Open J. Robot. Auton. Decis.-Mak. Hum.-Mach. Interact. 10, 1–8 (2025).

    Google Scholar 

  49. Cheng, G. et al. Spatial difference analysis for accessibility to high level hospitals based on travel time in Shenzhen, China. Habitat Int. 53, 485–494. https://doi.org/10.1016/j.habitatint.2015.12.023 (2016).

    Google Scholar 

  50. Khattak, A. J., Yim, Y. & Prokopy, L. S. Willingness to pay for travel information. Transp. Res. Part C Emerg. Technol. 11, 137–159. https://doi.org/10.1016/S0968-090X(03)00005-6 (2003).

    Google Scholar 

  51. Zhao, K., Musolesi, M., Hui, P., Rao, W. & Tarkoma, S. Explaining the power-law distribution of human mobility through transportationmodality decomposition. Sci. Rep. 5, 9136. https://doi.org/10.1038/srep09136 (2015).

    Google Scholar 

  52. Tang, J., Zhao, C., Liu, F., Hao, W. & Gao, F. Analyzing travel destinations distribution using large-scaled GPS trajectories: A spatio-temporal Log-Gaussian Cox process. Phys. A Stat. Mech. Appl. 599, 127305. https://doi.org/10.1016/j.physa.2022.127305 (2022).

    Google Scholar 

  53. Wang, Z., Li, S. & Zhang, Y. Which distance-decay function can improve the goodness of fit of the metro station ridership regression model? A Case Study of Beijing. Buildings 15, 1686. https://doi.org/10.3390/buildings15101686 (2025).

    Google Scholar 

  54. Jiang, H., Liu, R., Luo, S., Yi, D. & Zhang, J. Understanding metro station areas’ functional characteristics via embedding representation: A case study of shanghai. Sci. Rep. 15, 2725. https://doi.org/10.1038/s41598-025-87336-6 (2025).

    Google Scholar 

  55. Luo, J. et al. Analysis of city centrality based on entropy weight TOPSIS and population mobility: A case study of cities in the Yangtze River Economic Belt. J. Geogr. Sci. 30, 515–534. https://doi.org/10.1007/s11442-020-1740-9 (2020).

    Google Scholar 

  56. Qi, Y., Fan, Y., Sun, T. & Hu, L. I. Decade-long changes in spatial mismatch in Beijing, China: Are disadvantaged populations better or worse off?. Environ. Plann. Econ. Space 50, 848–868. https://doi.org/10.1177/0308518X18755747 (2018).

    Google Scholar 

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Acknowledgements

The authors also thank the Shanghai Metro Company for providing the smart card data and Amap (Gaode) for the POI data.

Funding

This research was funded by the Education and Teaching Reform Project of Hunan Urban Professional College, grant number DS2025JG008.

Author information

Authors and Affiliations

  1. College of Public Administration and Law, Hunan Agricultural University, Changsha, 410128, China

    Pei Jiang & Siqi Lin

  2. Jingdong Digital Commerce College, Hunan Urban Professional College, Changsha, 410137, China

    Siqi Lin

  3. Shenzhen General Integrated Transportation and Municipal Engineering Design & Research Institute Co., Ltd., Shenzhen, 518100, China

    Pei Jiang, Yawen Liu, Xuewen Shi, Hui Zhuo & Yuntao Liu

  4. Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT, UK

    Yawen Liu

  5. Cities Planning & Design, Ove Arup Group Limited, Leeds, LS1 4AP, UK

    Min Erin Fu

  6. Beijing Municipal Transportation Operations Coordination Center, Beijing Municipal Commission of Transport, Beijing, 101160, China

    Shijun Cheng

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Contributions

P.J., Y.-W.L., X.S., Y.-T.L. and S.L conceived the experiments, P.J., Y.L., H.Z. and S.C. conducted the experiments, P.J., Y.L., X.S., M.E.F., S.L. analysed the results. All authors reviewed and revised the manuscript.

Corresponding author

Correspondence to Siqi Lin.

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Jiang, P., Liu, Y., Shi, X. et al. Measuring spatial equity between metro accessibility and public service demand in Shanghai using a data-driven framework. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40160-y

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  • Received: 09 December 2025

  • Accepted: 10 February 2026

  • Published: 13 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-40160-y

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Keywords

  • Metro accessibility
  • Demand patterns
  • Equitable mobility
  • Supply-demand alignment
  • Smart card data
  • Big-data-driven assessment
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