Does ridesharing affect road safety? the introduction of Moto-Uber and other factors in the Dominican Republic
Bibliographic record
Abstract
Annually, more than 379 000 motorcycle occupants across the world die in motor-vehicle collisions—84% of these fatalities occurred in Low- and Middle-Income countries. Recent studies suggest that the Uber’s four-wheeler ride-sharing service (UberCAR) may reduce traffic fatalities. However, research has not considered how Uber’s two-wheeler ridesharing service (UberMOTO) might affect traffic-motorcycle fatalities. Monthly counts of car occupant and motorcycle fatalities from the Dominican Republic, a country in which both Uber services have been introduced, were collected from the Ministry of Public Health. We conducted interrupted time-series analyses using monthly traffic fatalities per 100,000 population for the period 2012–2018. We studied Santo Domingo and Santiago, the only two cities in which UberCAR and UberMOTO were launched in different times. The introduction of UberMOTO was associated with a 0.16 short-term decrease (95% CI -0.29 to −0.05) in the level of monthly motorcycle fatalities per 100,000 population in Santo Domingo, and a 0.34 decrease (95% CI −0.68, 0.00) in Santiago. UberCAR was associated with an increase of 0.03 (95% CI −0.06 to 0.13) in the level of monthly car occupant fatalities per 100,000 population in Santo Domingo, and with a 0.20 increase (95% CI 0.05 to 0.35) in Santiago. After Santo Domingo and Santiago introduced UberMOTO and UberCAR services, we observed short-term decreases in motorcycle fatalities and abrupt increases in car fatalities, respectively. These associations of ridesharing services with traffic fatalities vary between cities and over time, which might reflect differences in specific city features, including characteristics of the vehicle fleet and public transportation systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".