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Public transport accessibility in villages in and around major Chinese cities

Nature Cities volume 2, pages 749–758 (2025)Cite this article

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Abstract

Public transport (PT) accessibility is crucial to inclusive, sustainable urban development, as codified in United Nations Sustainable Development Goal (SDG) 11. Growth in PT accessibility can mask wide variation near and within even major cities. Contrary to the 2023 SDG report’s claim of over 80% convenient PT access in major Chinese cities, we find that only two cities’ villages meet the benchmarks and lowest rate, at just 34%. Commute time improvements depend heavily on population dynamics, but new bus station placements often fail to reflect these shifts. Meanwhile, although fares to city centers generally decreased, the proportion of village populations covered by high-fare routes rose noticeably in some cities. These results reveal how additional travel time, higher costs and changing demographics can impede villagers’ access to urban services. The narrow focus on a ‘10-minute’ performance metric risks driving suboptimal planning decisions that overlook broader travel burdens. A more nuanced, population-responsive approach to planning is needed to ensure that SDG 11 remains inclusive for all urban communities.

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Fig. 1: Distribution of study area with main transit routes in 2023.
Fig. 2: Temporal and spatial evolution of bus stations and distribution of villages in key Chinese cities, 2017–2023.
Fig. 3: Proportions and numbers of village populations with convenient walking accessibility, 2017–2023.
Fig. 4: Improvements in city center accessibility across major Chinese cities, 2017–2023.

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Data availability

The minimum dataset supporting this study (2017–2023 village and road-network shapefiles for the ten studied cities) is available via Zenodo at https://doi.org/10.5281/zenodo.15541605 (ref. 50). Additional route and station data are available via OSM at https://www.openstreetmap.org/ and were supplemented with data extracted from AutoNavi using a custom Python script (BeautifulSoup4 v.4.10.0). Village data are derived from the official website of the Chinese National People’s Government and are available via the Chinese National People’s Government at http://english.www.gov.cn/, with data samples from the sixth batch of traditional villages available at the Chinese National People’s Government via https://www.gov.cn/zhengce/zhengceku/2023-03/21/content_5747708.htm (list in Chinese). The administrative area data for each village, public service centers data and typical bus departure frequency information were individually queried on local government websites. These data are available upon request from the authors. The WorldPop high-resolution population counts are available from WorldPop at https://hub.worldpop.org/geodata/listing?id=69.

Code availability

The Python script used to extract and process additional bus-route data from AutoNavi (Gaode Map) with BeautifulSoup4 (v.4.9.3) is available via Code Ocean at https://doi.org/10.24433/CO.2745477.v1.

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Acknowledgments

This work was supported by the National Social Science Fund of China, General Program (grant number 24BMZ054) to Z.C.

Author information

Authors and Affiliations

  1. Center for Chinese Nation Research, Guizhou Minzu University, Guiyang, China

    Zihua Chen

  2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Zihua Chen, Bingzhi Liu, Xiaohui Yang & Zhenbo Wang

  3. School of Urban Economics and Public Affairs, Capital University of Economics and Business, Beijing, China

    Xiaowei Li

  4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

    Bingzhi Liu, Xiaohui Yang & Zhenbo Wang

  5. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Shaohua Wang

  6. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, China

    Xiao Li

  7. School of Ethnology and Sociology, Minzu University of China, Beijing, China

    Jiaxin Li

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  1. Zihua Chen
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Contributions

Z.C. designed the study, drafted the initial paper and led the revisions. Xiaowei Li and B.L. conducted the data analysis. S.W. and Xiao Li were responsible for large-scale data collection and web scraping. X.Y. reviewed the analysis and contributed to the figure design. J.L. and Z.W. supervised the project. All authors reviewed and approved the final paper.

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Correspondence to Zhenbo Wang.

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Nature Cities thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Changes in public transport fares and high-fare coverage in major cities, 2017–2023.

Box-and-whisker plots (left y-axis) summarize the minimum single-trip fare (CNY) available at each village bus stop that offers at least one city-bound route (n = exact number of independent stops, printed below each city name). Center line = median; box = 25th–75th percentiles (IQR); whiskers = minimum and maximum observations; gray dots mark values lying more than 1.5 × IQR outside the box. Repeated timetable checks at the same stop were averaged and therefore do not constitute additional replicates. Blue circles (right y-axis) show the share of villagers whose nearest stop provides only high-fare services (starting fare > 5 CNY, for example tourist, express or inter-city buses). No box is shown for Wuhan in 2017 due to fewer than three qualifying stops. Cities are ordered (left-to-right) by the 2017–2023 change in population residing within a 10-minute walking-transit zone. Data collection and processing details are provided in Methods 5.3.4.

Source data

Extended Data Fig. 2 Standardized Distribution of Bus Station Growth and Population Change, 2017–2023.

a). Red and blue curves represent changes in bus station counts and population, respectively, within each radius ‘r’ band (excluding inner radii). Values are standardized using Z-score normalization for comparability. The density-weighted CV indicates the uniformity of these changes across 20 concentric rings, with lower values representing more even spatial distribution. The ‘avg. r’ value denotes the average village radius of each city. Duplicate stations with the same name were excluded. Cities are arranged from top to bottom according to changes in the population within 10-minute walking transit zone. b). Pearson correlation between ring-level population change and bus-station growth (2017–2023); two-sided Pearson r (df = 18), n = 20 concentric rings per city. Exact P values: Chongqing p = 0.0002 (0.350–0.866); Suzhou p < 0.0001 (0.668–0.942); Tianjin p = 0.061 (–0.714–0.055); Beijing p = 0.847 (–0.405–0.479); Chengdu p = 0.827 (–0.397–0.486); Wuhan p = 0.792 (–0.451–0.434); Hangzhou p = 0.378 (–0.556–0.312); Shanghai p = 0.048 (–0.030–0.726); Nanjing p = 0.0008 (0.444–0.891); Guangzhou p = 0.0003 (0.567–0.921). Numbers in parentheses denote the 95 % confidence interval for each r. No adjustment for multiple comparisons was applied.

Source data

Supplementary information

Reporting Summary

Supplementary Code 1.

Python script for bus-route data extraction.

Source data

Source Data Fig. 3.

Statistical data for 10_min_walking_transit population and proportions.

Source Data Fig. 4.

Statistical data for walking/riding/transfer/stopping time distributions.

Source Data Extended Data Fig. 1.

Statistical data for village-level fares and the proportions of residents served only by high-fare routes, underpinning the box-and-whisker plots and blue-dot percentages.

Source Data Extended Data Fig. 2.

Statistical data for ring-level station and population densities across 20 concentric buffers per village, used to derive the Z-score curves, density-weighted CVs and Pearson r coefficients.

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Chen, Z., Li, X., Liu, B. et al. Public transport accessibility in villages in and around major Chinese cities. Nat Cities 2, 749–758 (2025). https://doi.org/10.1038/s44284-025-00277-z

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