Correlation and causality between traffic congestion and the built environment: a case study in New York city
Bibliographic record
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".