Analyzing disparities in app-hailed travel during extreme heat in New York City
Bibliographic record
Abstract
• Ridership increased by six to nine percent compared to normal-weather days. • Fixed effects were used to analyze variation across low- and high-income areas. • Afternoon peak higher by 1.7 to 3.2 rides per 1,000 people in median income areas. • Ridership increased by three percent for every $10,000 increase in income. • Baseline per cap trips were lower in low-income areas compared to high-income areas. To understand extreme weather effects on travel behavior, we examine changes in New York City app-hailed travel during extreme-heat days and estimate whether such changes vary across low- and high-income neighborhoods. We use trip records for July 2019, when several heat-related messages were issued by the National Weather Services, and find that daily ridership was six to nine percent higher on days when heat messages were issued than on matching weekdays in the same month. Our fixed effects regression models estimate that for median income neighborhoods afternoon peak-hour ridership was 1.7 to 3.2 rides higher per 1,000 people during the heat events than on matching weekdays in the same month. This effect increased by an additional 0.5 to 0.6 rides per 1,000 people for every $10,000 increase in average neighborhood per capita income, suggesting that higher income travelers increased trip frequency at a higher rate than lower-income travelers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".