Research map / Learning theory and algorithms / Fairness
Balancing Efficiency and Fairness in On-Demand Ridesourcing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply REASSIGN with the highest fairness threshold instead of the efficiency-only assignment | ridesourcing assignment · NYC taxi single-batch worst case | minimum driver utility: 51 -> 328 | Helped |
| apply REASSIGN toward the fairest solution (lambda=1) | ridesourcing assignment · NYC taxi single-batch worst case | efficiency E(M)/Eopt: loss less than 6% | Hurt |
| increase lambda from 0 to 1 in REASSIGN | multi-period assignment, 10 days · NYC taxi multi-period | F(M)/Fopt | Helped |
| increase lambda to 1 in REASSIGN | multi-period assignment · NYC taxi multi-period | efficiency E(M)/Eopt | No clear effect |
Closest papers by meaning
- Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours (AAAI 2020 · similarity 0.89)
- Real-Time Driver-Request Assignment in Ridesourcing (AAAI 2022 · similarity 0.88)
- Future Aware Pricing and Matching for Sustainable On-Demand Ride Pooling (AAAI 2023 · similarity 0.86)
- Algorithms for Trip-Vehicle Assignment in Ride-Sharing (AAAI 2018 · similarity 0.85)
- Optimizing Ride-Pooling Operations with Extended Pickup and Drop-Off Flexibility (AAAI 2026 · similarity 0.85)
- i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance (AAAI 2024 · similarity 0.80)
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