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Bidirectional yet asymmetric causality between urban systems and traffic dynamics in 30 cities worldwide

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Abstract

Understanding how urban systems and traffic dynamics co-evolve is crucial for advancing sustainable and resilient cities. However, their bidirectional causal relationships remain underexplored due to challenges of simultaneously inferring spatial heterogeneity, temporal variation, and feedback mechanisms. Here we present a spatio-temporal causality framework that bridges correlation and causation by integrating spatio-temporal weighted regression with spatio-temporal convergent cross-mapping. Characterizing cities through urban structure, form, and function, the framework uncovers bidirectional causal patterns between urban systems and traffic dynamics across 30 cities on six continents. Our findings reveal asymmetric bidirectional causality, with urban systems exerting stronger influences on traffic dynamics than the reverse in most cities. Urban form and function shape mobility more profoundly than structure, even though structure often exhibits higher correlations. This does not preclude the reversed causal direction, whereby long-established mobility patterns can also reshape the built environment over time. Finally, we identify three causal archetypes: tightly coupled, pattern-heterogeneous, and workday-attenuated, which support city-to-city learning and inform context-sensitive strategies in sustainable urban and transport planning.

Data availability

The traffic datasets used to compute traffic dynamics indicators can be downloaded through the HERE API (https://www.here.com/developer). OSM and GADM data, used for computing urban system metrics, can be publicly accessed from https://download.geofabrik.de/and https://gadm.org/data.html, respectively. The urban and traffic feature data generated in this study have been deposited in Figshare: https://doi.org/10.6084/m9.figshare.28656800. Source data are provided with this paper.

Code availability

All codes that support the findings of this study are available in Figshare via the following link: https://doi.org/10.6084/m9.figshare.28656800.

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Acknowledgements

The research was conducted at the Future Resilient Systems at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and the National Research Foundation Singapore. This research is supported by the National Research Foundation Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme (Y.Z., M.R.).

Funding

Open access funding provided by Swiss Federal Institute of Technology Zurich.

Author information

Authors and Affiliations

  1. Future Resilient Systems, Singapore-ETH Centre, ETH Zurich, Singapore, Singapore

    Yatao Zhang & Martin Raubal

  2. Institute of Cartography and Geoinformation, ETH Zurich, Zurich, Switzerland

    Yatao Zhang, Ye Hong & Martin Raubal

  3. Department of Geography, University College London, London, UK

    Yatao Zhang

  4. Geospatial Data Science Lab, Department of Geography, University of Wisconsin-Madison, Madison, WI, USA

    Song Gao

Authors

  1. Yatao Zhang
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  2. Ye Hong
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  3. Song Gao
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  4. Martin Raubal
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Contributions

Y.Z. and M.R. conceived the study. Y.Z. developed the methodology, implemented the software, and wrote the original draft. Y.Z. and Y.H. conducted the analysis. All authors reviewed and edited the manuscript. M.R. and S.G. supervised the project.

Corresponding author

Correspondence to
Yatao Zhang.

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Nature Communications thanks Klavdiya Bochenina and Luca Pappalardo for their contribution to the peer review of this work. A peer review file is available.

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Zhang, Y., Hong, Y., Gao, S. et al. Bidirectional yet asymmetric causality between urban systems and traffic dynamics in 30 cities worldwide.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-71377-0

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