Highlights Abstracting the vehicle dispatching problem as a load balancing problem. Solving the challenge of concurrent requests with the help of recommendation system. Designing the DRL method based on the data of a real ride-hailing platform.
Abstract The vehicle dispatching system is one of the most critical problems in online ride-hailing platforms, which requires adapting the operation and management strategy to the dynamics of demand and supply. In this paper, we propose a single-agent deep reinforcement learning approach for the vehicle dispatching problem called deep dispatching, by reallocating vacant vehicles to regions with a large demand gap in advance. The simulator and the vehicle dispatching algorithm are designed based on industrial-scale real-world data and the workflow of online ride-hailing platforms, ensuring the practical value of our approach. Besides, the vehicle dispatching problem is translated in analogy with the load balancing problem in computer networks. Inspired by the recommendation system, the problem of high concurrency of dispatching requests is addressed by sorting the actions as a recommendation list, whereby matching action with requests. Experiments demonstrate that the proposed approach is superior to existing benchmarks. It is also worth noting that the proposed approach won first place in the vehicle dispatching task of KDD Cup 2020.
Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
2022-03-27
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Systems and methods for dispatching shared rides through ride-hailing platform
Europäisches Patentamt | 2023
|SYSTEMS AND METHODS FOR DISPATCHING SHARED RIDES THROUGH RIDE-HAILING PLATFORM
Europäisches Patentamt | 2022
|Order-Dispatching Strategy Induced by Optimal Transport Plan for an Online Ride-Hailing System
Transportation Research Record | 2022
|