Research map / Learning theory and algorithms / Submodular and game algorithms
Algorithms for Trip-Vehicle Assignment in Ride-Sharing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase number of drivers n (10 -> 50) | Algorithm 1 · Uniform synthetic, B=10 | approximation ratio upper bound (l1): 1.23 -> 1.10 | Helped |
| vary distance norm (l1 vs l2), B, centers, sigma | Algorithm 1 · synthetic uniform and Gaussian mixture | approximation ratio upper bound | No clear effect |
| more requests than drivers (m=3n instead of m=2n) | Algorithm 1 variant · Gaussian mixture synthetic | approximation ratio upper bound | Hurt |
Closest papers by meaning
- Optimizing Ride-Pooling Operations with Extended Pickup and Drop-Off Flexibility (AAAI 2026 · similarity 0.87)
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- Allocation Problems in Ride-Sharing Platforms: Online Matching With Offline Reusable Resources (AAAI 2018 · similarity 0.86)
- Balancing Efficiency and Fairness in On-Demand Ridesourcing (NeurIPS 2019 · similarity 0.85)
- Community-Based Trip Sharing for Urban Commuting (AAAI 2018 · similarity 0.85)
- Neural Approximate Dynamic Programming for On-Demand Ride-Pooling (AAAI 2020 · similarity 0.85)
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