Work

DeepPool: Distributed Model-Free Algorithm for Ride-Sharing Using Deep Reinforcement Learning

Year: 2019

Type: article

Abstract: 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 paper, 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, 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.

Source: IEEE Transactions on Intelligent Transportation Systems

DOI: 10.1109/tits.2019.2931830

Authors: Abubakr O. Al-Abbasi, Arnob Ghosh, Vaneet Aggarwal

Institution: Purdue University West Lafayette

Language: English

Topic: Transportation and Mobility Innovations

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: National Science Foundation