Dynamic Real-Time Ridesharing: A Literature Review and Early Findings from a Market Demand Study of a Dynamic Transportation Trading Platform for the University of Calgary's Main Campus
Bibliographic record
Abstract
The objective of this paper is twofold. The first part provides a comprehensive literature review on the state of the art and the state of practice of dynamic real-time ridesharing systems. The second part presents early findings from a survey on a social-network enabled dynamic peer-to peer dynamic ridesharing system called “FacePorter” at the University of Calgary, Alberta. A survey with a combination of revealed and stated preferences was conducted in order to evaluate the propensity among university employees and students towards participation in the FacePorting program. The survey‟s results confirmed some of the previous findings pointing to the influential factors in the success of peer-to-peer dynamic ridesharing, such as socio-demographic characteristics, attitudinal and behavioral factors, weather condition, ridesharing contribution fee, riders‟ profile and required incremental driving time. Furthermore, the survey‟s outcomes confirm that students are more willing to partake as ride-seekers in real-time ridesharing programs compared to university academic staff and employees. On the contrary, academic staff and employees seem to be more interested in offering rides.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".