DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning
This work addresses the operational efficiency and profitability for ride-sharing companies, though it is incremental as it builds on existing deep reinforcement learning methods.
The paper tackles the problem of optimizing vehicle dispatch for ride-sharing platforms to improve profit and resource efficiency, achieving better performance than existing strategies that ignore ride-sharing or future demand anticipation using a real-world NYC taxi dataset.
The success of modern ride-sharing platforms crucially depends on the profit of the ride-sharing fleet operating companies, and how efficiently the resources are managed. Further, ride-sharing allows sharing costs and, hence, reduces the congestion and emission by making better use of vehicle capacities. In this work, we develop a distributed model-free, DeepPool, that uses deep Q-network (DQN) techniques to learn optimal dispatch policies by interacting with the environment. Further, DeepPool efficiently incorporates travel demand statistics and deep learning models to manage dispatching vehicles for improved ride sharing services. Using real-world dataset of taxi trip records in New York City, DeepPool performs better than other strategies, proposed in the literature, that do not consider ride sharing or do not dispatch the vehicles to regions where the future demand is anticipated. Finally, DeepPool can adapt rapidly to dynamic environments since it is implemented in a distributed manner in which each vehicle solves its own DQN individually without coordination.