Rebalancing Self-Interested Drivers in Ride-Sharing Networks to Improve Customer Wait-Time
Bibliographic record
Abstract
In this article, we address the problem of controlling self-interested drivers in ride-sharing applications. The objective of the ride-sharing company is to improve the customer experience by minimizing the wait-time before pickup. Meanwhile, the drivers attempt to maximize their profit by choosing the best location to wait in the environment between the ride requests assigned to them. The objectives of the ride-sharing company and the drivers are not aligned, and the company has no direct control over the waiting locations of the drivers. The focus of this article is to provide two indirect control methods for the ride-sharing company to optimize the set of waiting locations of the drivers, thereby minimizing one of two objectives: 1) the expected wait-time of the customers or 2) the maximum wait-time of customers. The proposed indirect control methods are: 1) sharing information to a subset of the drivers on the location of other waiting drivers and 2) paying drivers to relocate. We show that the problem of finding the optimal control is NP-hard for both objectives and both control methods. For the information sharing method, we provide an LP-rounding algorithm to minimize the expected wait-time and a three-approximation algorithm to minimize the maximum wait-time. To incentivize the drivers to relocate with payments, we provide three-approximation algorithms for both objectives. Finally, we evaluate the proposed control methods on real-world data and show that we can achieve up to 80% improvement for both objectives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".