Subsidy Strategies of Ride-Hailing Platforms Considering Taxi Street-Hailing during Order Overflow
Bibliographic record
Abstract
The emergence of ride-hailing services has revolutionized the transportation industry for passengers, prompting taxi services to evolve from the conventional method of street-hailing to a combined "online-offline" operational approach.In this new model, taxis combine on-street pickups with platform-based orders.When market supply and demand are imbalanced, leading to excess orders, taxis prioritize street-hailing for faster customer acquisition.Meanwhile, ride-hailing platforms address surging passenger demand by offering subsidies to attract more vehicles to participate in online dispatching.This study focuses on the strategic choices of ride-hailing platforms and taxis during order overflow scenarios.An evolutionary game model is constructed to simulate taxi streethailing behavior under such conditions.Simulations are conducted to generate interpolation-based probability curves, including the probability of taxis accepting offline orders and the probability of regional orders being served.These findings offer recommendations for ride-hailing platforms on designing subsidy strategies in response to changes in regional order density.Additionally, the study examines how factors such as order distance, passenger-seeking costs, and platform commission rates influence taxis' order acceptance strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".