# An Optimization Framework For Online Ride-sharing Markets

arXiv: https://arxiv.org/abs/1612.03797 · HTML: https://mlsanity.com/paper/1612.03797/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2016.

## Built on by (most cited first, 2 of 2)

- [Smoothed Online Combinatorial Optimization Using Imperfect Predictions](https://mlsanity.com/paper/2204.10979/) (2022)
- [Towards Fairness in Online Service with k Servers and its Application on Fair Food Delivery](https://mlsanity.com/paper/2312.11280/) (2023)

## Abstract (arXiv)

Taxi services and product delivery services are instrumental for our modern society. Thanks to the emergence of sharing economy, ride-sharing services such as Uber, Didi, Lyft and Google's Waze Rider are becoming more ubiquitous and grow into an integral part of our everyday lives. However, the efficiency of these services are severely limited by the sub-optimal and imbalanced matching between the supply and demand. We need a generalized framework and corresponding efficient algorithms to address the efficient matching, and hence optimize the performance of these markets. Existing studies for taxi and delivery services are only applicable in scenarios of the one-sided market. In contrast, this work investigates a highly generalized model for the taxi and delivery services in the market economy (abbreviated as"taxi and delivery market") that can be widely used in two-sided markets. Further, we present efficient online and offline algorithms for different applications. We verify our algorithm with theoretical analysis and trace-driven simulations under realistic settings.