Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms
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.
Cite
Text
Zhao et al. "Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/652Markdown
[Zhao et al. "Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/zhao2022ijcai-dynamic/) doi:10.24963/IJCAI.2022/652BibTeX
@inproceedings{zhao2022ijcai-dynamic,
title = {{Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms}},
author = {Zhao, Zishuo and Chen, Xi and Zhang, Xuefeng and Zhou, Yuan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {4701-4708},
doi = {10.24963/IJCAI.2022/652},
url = {https://mlanthology.org/ijcai/2022/zhao2022ijcai-dynamic/}
}