Bibliographic record
Abstract
UberHOP is a commute-focused interpretation of the Uber suite of transportation services, with the goal of reducing personal vehicle commute trips. The service first launched in Seattle, Washington, and Toronto, Ontario, Canada, in December 2015 and expanded to Manila, Philippines, in early 2016. UberHOP is similar to vanpooling with fixed pickup and drop-off locations in the primary commute direction during peak hours, but it leverages Uber’s ridesourcing platform to replace fixed departure schedules with riders matched in real time. This paper reports on an intercept survey (83% response rate) to understand who rode, how they traveled to the pickup location, why they rode, and what modes UberHOP was replacing for all 11 UberHOP routes in Seattle during the morning and evening commute periods. In addition, detailed trip and total rider count data were collected during the survey administration process. The results show that many UberHOP riders made UberHOP their primary form of commute mode. Unlike standard ridesourcing services, UberHOP riders predominantly replaced public transportation modes rather than personal vehicles. UberHOP services were canceled in Seattle in August 2016. However, with larger rider densities per trip, the UberHOP model can be profitable, and it is reasonable to expect that Uber or others will resurrect a similar service in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.322 | 0.100 |
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".