Research map / Learning theory and algorithms / Fairness
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the LP-based algorithm instead of Greedy | Rideshare matching · NYC taxi dataset | profit | Helped |
| Use the LP-based algorithm instead of Greedy | Rideshare matching · NYC taxi dataset | fairness | Helped |
| Use the LP-based algorithm instead of Uniform | Rideshare matching · NYC taxi dataset | profit | Helped |
| Use the LP-based algorithm instead of Uniform | Rideshare matching · NYC taxi dataset | fairness | Helped |
| Use the LP-based algorithm instead of Greedy | Rideshare matching · Synthetic dataset | profit | Helped |
| Use the LP-based algorithm instead of Greedy | Rideshare matching · Synthetic dataset | fairness | Helped |
| Use the LP-based algorithm instead of Uniform | Rideshare matching · Synthetic dataset | profit | Helped |
| Use the LP-based algorithm instead of Uniform | Rideshare matching · Synthetic dataset | fairness | Helped |
| Increase the weight on the profit LP solution (alpha) while decreasing the weight on the fairness LP solution (beta) (ablation) | LP-based algorithm · Extreme case, one driver and three request types | fairness | Hurt |
| Increase the weight on the fairness LP solution (beta) while decreasing the weight on the profit LP solution (alpha) (ablation) | LP-based algorithm · Extreme case, one driver and three request types | fairness | Helped |
Closest papers by meaning
- Balancing Efficiency and Fairness in On-Demand Ridesourcing (NeurIPS 2019 · similarity 0.89)
- Real-Time Driver-Request Assignment in Ridesourcing (AAAI 2022 · similarity 0.87)
- Future Aware Pricing and Matching for Sustainable On-Demand Ride Pooling (AAAI 2023 · similarity 0.87)
- Allocation Problems in Ride-Sharing Platforms: Online Matching With Offline Reusable Resources (AAAI 2018 · similarity 0.87)
- Time Fairness in Online Knapsack Problems (ICLR 2024 · similarity 0.83)
- Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions (NeurIPS 2024 · similarity 0.82)
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