Large-Scale Order Dispatch in On-Demand Ride-Hailing Platforms: A Learning and Planning Approach

By Zhe Xu, Zhixin Li, Qingwen Guan, Di Zhang, Qiang Li, Junxiao Nan, Chunyang Liu, Wei Bian, Jieping Ye. Knowledge Discovery and Data Mining, 2018

444 citations recorded by the source corpus.

We present a novel order dispatch algorithm in large-scale on-demand ride-hailing platforms. While traditional order dispatch approaches usually focus on immediate customer satisfaction, the proposed algorithm is designed to provide a more efficient way to optimize resource utilization and user experience in a global and more farsighted view. In particular, we model order dispatch as a large-scale sequential decision-making problem, where the decision of assigning an order to a driver is determined by a centralized algorithm in a coordinated way. The problem is solved in a learning and planning manner: 1) based on historical data, we first summarize demand and supply patterns into a spatiotemporal quantization, each of which indicates the expected value of a driver being in a particular state; 2) a planning step is conducted in real-time, where each driver-order-pair is valued in consideration of both immediate rewards and future gains, and then dispatch is solved using a combinatorial optimizing algorithm. Through extensive offline experiments and online AB tests, the proposed approach delivers remarkable improvement on the platform's efficiency and has been successfully deployed in the production system of Didi Chuxing.

Subfields: Automotive Engineering, Marketing, Transportation

View Paper: https://www.semanticscholar.org/paper/a4fd86ad5deac4e56b561b920bd7b84c3854ac84

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Hai Wang
2. Zhenhui Jessie Li
3. Hai Yang

In Marketing

1. Hai Wang
2. Yong Yu
3. Weinan Zhang
4. Hai Yang

In Transportation

1. Hai Wang
2. Weinan Zhang
3. Zhenhui Jessie Li
4. Huichu Zhang
5. Haitao Yuan
6. Hai Yang
7. Guoliang Li

Citations in the corpus

• CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario (2019, methodology): “e as a benchmark reinforcement learning environment for transportation studies. Besides traffic signal control, reinforcement learning has been used in transportation studies such as taxi dispatching [12] and mixed autonomy systems [11]. But all the existing studies either use SUMO or over-simplifie”
• Ridesourcing systems: A framework and review (2019, background): “Xu et al. (2018a) study the factors that affect taxi drivers’ response to ridesourcing requests.”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “[190] first propose an RL-based algorithm to dispatch resource in a”