Research map / Bandits and online learning / Online convex optimization and games
Online Spatio-Temporal Matching in Stochastic and Dynamic Domains
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use two-stage stochastic matching with future demand samples instead of myopic greedy/one-step matching | taxi matching · Dataset1 8:00 AM | competitive ratio (revenue, requests): more than 85% of optimal revenue at 11 samples | Helped |
| use full TSS instead of Benders decomposition (3 iterations) (ablation) | two-stage matching · Dataset1 8:00 AM | revenue: +5% revenue, +10% requests | Helped |
| use Benders decomposition instead of full TSS (ablation) | two-stage matching · Dataset2 | solution quality | No clear effect |
| use Benders decomposition instead of full TSS (ablation) | two-stage matching · Dataset1 runtime | runtime: TSS up to 100 seconds | Helped |
| increase number of taxis (reduce scarcity) | TSS/BD vs myopic · Dataset1 | gain over myopic | Hurt |
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