Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling
Abstract
Our research focuses on developing matching policies that match drivers and riders for ride-pooling services. We aim to develop policies that balance efficiency and various forms of fairness. We did this through two methods: new matching algorithms that include a fairness term in the objective function, and income redistribution methods based on the Shapley value of a driver. I tested these methods on New York City Taxicab data to evaluate their performance and found that they succeed in reducing certain forms of fairness.
Cite
Text
Raman. "Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17985Markdown
[Raman. "Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/raman2021aaai-investigating/) doi:10.1609/AAAI.V35I18.17985BibTeX
@inproceedings{raman2021aaai-investigating,
title = {{Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling}},
author = {Raman, Naveen},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {15978-15979},
doi = {10.1609/AAAI.V35I18.17985},
url = {https://mlanthology.org/aaai/2021/raman2021aaai-investigating/}
}