Paper Info
Title
Optimizing Long-Term Efficiency and Fairness in Ride-Hailing via Joint Order Dispatching and Driver Repositioning
Abstract
The ride-hailing service offered by mobility-on-demand platforms, such as Uber and Didi Chuxing, has greatly facilitated people's traveling and commuting, and become increasingly popular in recent years. Efficiency (e.g., gross merchandise volume) has always been an important metric for such platforms. However, only focusing on the efficiency inevitably ignores the fairness of driver incomes, which could impair the sustainability of the overall ride-hailing system in the long run. To optimize the aforementioned two essential metrics, order dispatching and driver repositioning play an important role, as they impact not only the immediate, but also the future order-serving outcomes of drivers. Thus, in this paper, we aim to exploit joint order dispatching and driver repositioning to optimize both the long-term efficiency and fairness for ride-hailing platforms. To address this problem, we propose a novel multi-agent reinforcement learning framework, referred to as JDRL, to help drivers make distributed order selection and repositioning decisions. Specifically, to cope with the variable action space, JDRL segments the action space into a fixed number of action groups, and fixes the policy output dimension for order selection as the number of action groups. In terms of the fairness criterion, JDRL adopts the max-min fairness, and augments the vanilla policy gradient to an iterative training algorithm that alternates between a minimization step and a policy improvement step to maximize both the worst and the overall performance of agents. In addition, we provide the theoretical convergence guarantee of our JDRL training algorithm even under non-convex policy networks and stochastic gradient updating. Extensive experiments are conducted with three public real-world ride-hailing order datasets, including over 2 million orders in Haikou, China, over 5 million orders in Chengdu, China, and over 6 million orders in New York City, USA. Experimental results show that JDRL demonstrates a consistent advantage compared to state-of-the-art baselines in terms of both efficiency and fairness. To the best of our knowledge, this is the first work that exploits joint order dispatching and driver repositioning to optimize both the long-term efficiency and fairness in a ride-hailing system.
Year
DOI
Venue
2022
10.1145/3534678.3539060
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Jiahui Sun111.03
Haiming Jin210412.12
Zhaoxing Yang301.69
lu su4111866.61
Xinbing Wang52642214.43