The Who, Why, and When of Uber and other Ride-hailing Trips: An Examination of a Large Sample Household Travel Survey
Bibliographic record
Abstract
Convenience and low prices have enabled ride-hailing companies, such as Uber and Lyft, to position themselves amongst the most valuable companies within the transportation sector. They now account for the lion share of activities in the platform economy and play an increasing role within our cities. Despite this, very little is known about the type of people that use them, nor the purpose and timing of trips. In addition to this, their effect on other modes, such as taxis and public transit, remains, for the most part, widely unexplored. By comparing the socioeconomic and trip characteristics of ride-hailing users to that of other mode users, we find ride-hailing to be a wealthy younger generation phenomenon. While our results show that ride-hailing is too minute and inconsequential to influence the ridership level of other more substantial modes of travel overall, when considering specific market segments, the rise of ride-hailing corresponds to a significant decrease in taxi ridership and a rise in active modes of travel. Moreover, due to the specific age, timing, and purpose of our subsample, we believe that ride-hailing may effectively reduce drunk-driving, and are convinced that as this mode increases in importance in the future, it will have a much more pronounced effect on the level of ridership of other modes as well.
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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 | 0.000 | 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".