Jin, J;
Zhou, M;
Zhang, W;
Li, M;
Guo, Z;
Qin, Z;
Jiao, Y;
... Ye, J; + view all
(2019)
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms.
In:
CIKM '19: Proceedings of the 28t ACM International Conference on Information and Knowledge Management.
(pp. pp. 1983-1992).
ACM: Beijing, China.
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Abstract
How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decisionmaking task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multihead attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of citywide hybrid order dispatching and fleet management over strong baselines.
| Type: | Proceedings paper |
|---|---|
| Title: | CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms |
| Event: | 28th ACM International Conference on Information and Knowledge Management (CIKM) |
| Location: | Beijing, PEOPLES R CHINA |
| Dates: | 03 November 2019 - 07 November 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3357384.3357978 |
| Publisher version: | https://doi.org/10.1145/3357384.3357978 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Hierarchical Reinforcement Learning; Multi-agent Reinforcement Learning; Ride-Hailing; Order Dispatching; Fleet Management |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10113550 |
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