Learning to assign: Towards fair task assignment in large-scale ride hailing

• Main Authors: SHI, Dingyuan, TONG, Yongxin, ZHOU, Zimu, SONG, Bingchen, LV, Weifeng, YANG, Qiang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: fairness; ride hailing; reinforcement learning; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/6382; https://ink.library.smu.edu.sg/context/sis_research/article/7385/viewcontent/kdd21_shi.pdf
• Institution: Singapore Management University

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