Ridehailing use, travel patterns and multimodality: A latent-class cluster analysis of one-week GPS-based travel diaries in California
Bibliographic record
Abstract
• Utilize two large GPS-based travel diary datasets collected from California residents. • Identify four traveler groups: drive-alone users, carpoolers, transit users and cyclists. • Each traveler group has distinctive characteristics and modality style. • Transit users have the highest rate of ridehailing adoption and usage. • Travelers substitute ridehailing for their most-used travel modes. Based on the analysis of one-week GPS-based travel diary data from the four largest metropolitan areas in California, this study performs a latent-class cluster analysis and identifies four distinctive traveler groups with varying levels of multimodality. These groups are characterized by their distinctive use of five travel modes (i.e., single-occupant vehicles, carpooling, public transit, biking, and walking) for both work and non-work trips. Two of these groups are more car-oriented and less multimodal (i.e., drive-alone users and carpoolers), whereas the other two are less car-oriented and display higher levels of multimodality (i.e., transit users and cyclists). Results from this study reveal the unique profiles of each traveler group in terms of their sociodemographic characteristics and built-environment attributes. The study further investigates the different characteristics of each traveler group in relation to ridehailing adoption, trip frequency and trip attributes. Transit users are found to have the highest rate of ridehailing adoption and usage. They are also more prone to use pooled ridehailing services in comparison to other groups. In terms of mode substitution, if ridehailing were not available, respondents tend to choose the mode they use most frequently. In other words, car-based travelers are more likely to substitute ridehailing trips with car trips, whereas non-car-based travelers are more likely to replace ridehailing with less-polluting modes. The findings from this study will prove valuable for transit agencies and policymakers interested in integrating ridehailing with other modes and promoting more multimodal and less car-dependent lifestyles.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".