Enhancing Random Regret Minimization With Perception and Demographic Heterogeneity Insights: A Taxi‐Hailing Case Study in Chengdu, China
Bibliographic record
Abstract
Due to the lack of consideration of heterogeneity in the traditional choice model based on regret theory, there may be errors in interpreting the real choice behavior. Traditional regret functions do not account for the perception of different alternatives and individual socioeconomic characteristics. This paper utilizes Weber’s law to explain the heterogeneity of travelers’ perceptions regarding alternative attributes. It introduces new parameters to consider individual socioeconomic characteristics to improve the classic random regret minimization (RRM) model. Then, these two improvements are incorporated into the model together. Different choice models are established based on random utility maximization (RUM) and RRM, respectively. This paper then takes taxi‐hailing choice behavior in Chengdu as an empirical study. The results suggest that the calibration results of different models are consistent, and the overall goodness of fit and hit rate of models under RRM are better than models under RUM. The improved RRM model considering both perception heterogeneity using Weber’s law and socioeconomic characteristics has the best model evaluation indexes. Thus, the improved model could better explain and predict travelers’ choice behavior.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".