Research map / Learning theory and algorithms / Submodular and game algorithms
Mechanism Design with Predicted Task Revenue for Bike Sharing Systems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use TruPreTar instead of Surge and Greedy under tight budget | TruPreTar · Mobike simulation, B=50, h=300m | revenue and profit | Helped |
| increase number of parking locations m | all mechanisms · Mobike simulation | revenue and profit | Helped |
| m below 30 with B=50: TruPreTar versus APP-OPT | TruPreTar · Mobike simulation, B=50 | revenue | No clear effect |
| sufficient budget B=500: TruPreTar versus APP-OPT | TruPreTar · Mobike simulation, B=500 | revenue | Hurt |
| m above 30 with B=50: Surge revenue | Surge · Mobike simulation, B=50 | revenue | No clear effect |
| increase acceptable distance h from 300m to 600m for Greedy | Greedy · Mobike simulation | revenue and profit | Helped |
| use TruPreTar instead of Greedy at h=600m, m=60 | TruPreTar · 100 rounds, m=60 | revenue variance: 648.1 -> 32.2 | Helped |
| use TruPreTar instead of Greedy at h=600m, m=60 | TruPreTar · 100 rounds, m=60 | maximum revenue: 161.3 -> 127.2 | Hurt |
| use TruPreTar instead of Greedy at h=600m, m=60 | TruPreTar · 100 rounds, m=60 | minimum revenue: 19.7 -> 98.8 | Helped |
Closest papers by meaning
- A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems (AAAI 2019 · similarity 0.86)
- Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction (AAAI 2021 · similarity 0.86)
- Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours (AAAI 2020 · similarity 0.86)
- Allocation Problems in Ride-Sharing Platforms: Online Matching With Offline Reusable Resources (AAAI 2018 · similarity 0.84)
- Optimal Mechanism in a Dynamic Stochastic Knapsack Environment (AAAI 2024 · similarity 0.82)
- Mechanism Design via the Interim Relaxation (NeurIPS 2025 · similarity 0.82)
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