Analyzing Ride-Sourcing Market Equilibrium and Its Transitions with Heterogeneous Users
Bibliographic record
Abstract
With the justification of a comprehensive matching function approach, this study analyzes the ride-sourcing market with heterogeneous users. A single origin-destination (O-D) ride-sourcing market model is first developed. The model fills the major research gap to extend general user heterogeneity modeling from the transit market to the two-sided ride-sourcing market. Sufficient conditions that guarantee a unique market equilibrium are given. Equilibrium market operation with a profit-maximizing platform is explored. Equilibrium transitions with respect to the changes in different exogenous market variables are investigated. Nonequilibrium modeling to understand the transition path is also analyzed, revealing how the detailed transition evolves. Extensions of the single O-D market model to aggregate and disaggregate markets and incorporation of travel time reliability are briefly discussed. Numerical experiments, which are based on real-world data in the city of Ningbo, China, and cover an aggregate/single O-D base case, sensitivity analysis, equilibrium transitions, and disaggregate market equilibrium, are presented to illustrate the theoretical model. The larger network of Sioux Falls is also tested as a demonstration of the disaggregate market equilibrium. Discussions are made regarding operation and policy implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".