An investigation of the factors that affect the use of pooled ridehailing services in California
Bibliographic record
Abstract
Pooled ridehailing services, like UberPOOL (now UberX Share) and Lyft Line (later rebranded as Lyft Shared before its discontinuation in May 2023), allow passengers to share part or the entirety of their trip with other paying passengers they typically do not know, resulting in lower fares. In this paper, we estimate a mixed logit model to examine the factors influencing the choice between pooled and solo ridehailing (e.g., UberX), using a dataset collected through rMove , an in-app survey and GPS data collection tool, which captures residents’ travel behaviors across three metropolitan regions in California. Our findings indicate that lower-income individuals, non-whites, women, and younger adults are more likely to choose pooled ridehailing. Conversely, individuals with higher vehicle ownership are less likely to pool. Frequent ridehailing users are more inclined to share rides, whereas employer-paid work trips are less likely to be pooled. Trips originating in high-density areas are also more likely to be pooled. Furthermore, we find a positive relationship between the use of public/active modes and the likelihood of pooling, highlighting both the risk of competition (and substitution) among these modes and an openness to multimodal travel among certain groups. Our analysis offers valuable insights for policymakers who seek to expand the share of pooled ridehailing trips while minimizing deadheading to reduce the emissions and congestion associated with the ridehailing industry.
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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 | 0.002 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 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".