Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
8-2021
Abstract
Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives. Despite extensive research on task assignment in ride hailing, the fairness of earnings among drivers is largely neglected. Pioneer studies on fair task assignment in ride hailing are ineffective and inefficient due to their myopic optimization perspective and timeconsuming assignment techniques. In this work, we propose LAF, an effective and efficient task assignment scheme that optimizes both utility and fairness. We adopt reinforcement learning to make assignments in a holistic manner and propose a set of acceleration techniques to enable fast fair assignment on large-scale data. Experiments show that LAF outperforms the state-of-the-arts by up to 86.7%, 29.1%, 797% on fairness, utility and efficiency, respectively
Keywords
fairness, ride hailing, reinforcement learning
Discipline
Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing
Research Areas
Software and Cyber-Physical Systems
Publication
KDD '21: Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Conference, August 14-18
ISBN
9781450383325
Identifier
10.1145/3447548.3467085
Publisher
Virtual Conference
City or Country
Singapore
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Numerical Analysis and Scientific Computing Commons