Name
Abstract | ||
|---|---|---|
A major challenge for ridesharing platforms is to guarantee profit and fairness simultaneously, especially in the presence of misaligned incentives of drivers and riders. We focus on the dispatching-pricing problem to maximize the total revenue while keeping both drivers and riders satisfied. We study the computational complexity of the problem, provide a novel two-phased pricing solution with revenue and fairness guarantees, extend it to stochastic settings and develop a dynamic (a.k.a., learning-while-doing) algorithm that actively collects data to learn the demand distribution during the scheduling process. We also conduct extensive experiments to demonstrate the effectiveness of our algorithms. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.24963/ijcai.2022/652 | International Joint Conference on Artificial Intelligence |
Keywords | DocType | Citations |
Planning and Scheduling: Planning Algorithms,Agent-based and Multi-agent Systems: Mechanism Design,AI Ethics, Trust, Fairness: Fairness & Diversity,Planning and Scheduling: Planning under Uncertainty,Planning and Scheduling: Planning with Incomplete Information | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zishuo Zhao | 1 | 0 | 0.68 |
| Xi Chen | 2 | 333 | 70.76 |
| Xuefeng Zhang | 3 | 0 | 0.34 |
| Yuan Zhou | 4 | 290 | 28.29 |