Correlates of Modal Substitution and Induced Travel of Ridehailing in California
Bibliographic record
Abstract
The availability of ridehailing services, such as Uber and Lyft, affects the way people choose to travel and can enable travel opportunities that were previously suppressed, leading to additional trips. Previous studies have investigated the modal substitution and induced travel caused by ridehailing, yet few have investigated the factors associated with these travel behaviors. Accordingly, this study examines the personal and trip characteristics associated with ridehailing users’ decisions to substitute other modes of travel or conduct new trips by ridehailing. Using detailed survey data collected in three California metropolitan regions from 2018 and 2019, we estimated an error components logit model of ridehailing users’ choice of an alternative travel option if ridehailing services were unavailable. We found that over 50% of ridehailing trips in our sample were replacing more sustainable modes (i.e., public transit, active modes, and carpooling) or were creating new vehicle miles, with a 5.8% rate of induced travel, with public transit being the most frequently substituted mode. Respondents without a household vehicle and who use pooled services were more likely to replace transit. Longer-distance ridehailing trips were less likely to replace walking, biking, or transit trips. Respondents identifying as a racial or ethnic minority or lacking a household vehicle were least likely to cancel a trip were ridehailing unavailable, suggesting their use of ridehailing for essential rather than discretionary purposes. Together, these findings provide valuable insights for policy makers seeking to address the environmental and equity issues associated with ridehailing.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".