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

TL;DR

This study introduces a novel spatio-temporal causality framework to analyze the bidirectional and asymmetric causal relationships between urban systems and traffic dynamics across 30 global cities, revealing key influences and archetypes.

Contribution

The paper develops a new causality framework combining weighted regression and convergent cross-mapping to uncover bidirectional causal patterns in urban traffic data.

Findings

Urban systems generally influence traffic dynamics more than vice versa.

Urban form and function impact mobility more than structural aspects.

Identified three causal archetypes: tightly coupled, pattern-heterogeneous, and workday-attenuated.

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. To address this gap, we propose a novel spatio-temporal causality framework that bridges correlation and causation by integrating spatio-temporal weighted regression with a newly developed spatio-temporal convergent cross-mapping approach. 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, as observed in cities such as Singapore, New Delhi, London, Chicago, and Moscow. 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 distinct causal archetypes: tightly coupled, pattern-heterogeneous, and workday-attenuated, which map pathways from causal diagnosis to intervention. This typology supports city-to-city learning and lays a foundation for context-sensitive strategies in sustainable urban and transport planning.