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Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms

Zishuo Zhao University of Illinois Urbana-Champaign Xi Chen New York University Xuefeng Zhang University of Illinois Urbana-Champaign Yuan Zhou Yau Mathematical Sciences Center, Tsinghua University

Machine Learning mathscidoc:2206.41012

International Joint Conference on Artificial Intelligence (IJCAI), 2022.7
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 dispatchingpricing 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.
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[ Download ] [ 2022-06-18 16:46:21 uploaded by zhouyuan ] [ 1036 downloads ] [ 0 comments ] 
@inproceedings{zishuo2022dynamic,
  title={Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms},
  author={Zishuo Zhao, Xi Chen, Xuefeng Zhang, and Yuan Zhou},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220618164621409594412},
  booktitle={ International Joint Conference on Artificial Intelligence (IJCAI)},
  year={2022},
}
Zishuo Zhao, Xi Chen, Xuefeng Zhang, and Yuan Zhou. Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms. 2022. In International Joint Conference on Artificial Intelligence (IJCAI). http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220618164621409594412.
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