An Optimization Framework For Online Ride-sharing Markets

TL;DR

This paper introduces a generalized optimization framework and algorithms for online and offline matching in ride-sharing markets, addressing the limitations of existing one-sided market models.

Contribution

It proposes a novel, comprehensive model for two-sided ride-sharing markets and develops efficient algorithms validated through theoretical analysis and simulations.

Findings

Algorithms improve matching efficiency in ride-sharing markets

Theoretical analysis confirms algorithm optimality

Simulations demonstrate practical effectiveness

Abstract

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.