Correlation and causality between traffic congestion and the built environment: a case study in New York city

Abstract

Traffic congestion is significantly affected by the built environment. Existing studies predominantly examine this through correlation analysis, overlooking causal mechanisms. This omission leads to unreliable feature selection in policy models and hinders evidence-based interventions. To address this, this study proposes a three-stage causal framework that rigorously assesses built environment impacts. The first stage identifies statistically significant correlations using multivariable least squares regression. The second stage applies five causal inference models – Granger causality, structural equation model, causal forest, causal impact, and convergent cross mapping – to uncover causality. The third stage assesses how the identified causal factors shape congestion patterns in perpetually congested roadways (PCRs). Applied to New York City (NYC), the United States, the results reveal 19 correlated and 11 causal impacts. Our key findings include: (1) Transit accessibility is the most robust causal factor, while built environment diversity exhibits time-dependent variability; (2) traffic light design demonstrates bidirectional causality with congestion; (3) PCRs exhibit four distinct spatiotemporal patterns, with bridge-related congestion having the most consistent impact. These results yielded policy recommendations for NYC transportation planning: (i) improve the first-and-last-mile connectivity through micro-mobility; (ii) deploy artificial intelligence-driven adaptive traffic signals; (iii) expand the capacity of critical bridge corridors near PCRs.

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