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

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

Research output: Contribution to journalArticlepeer-review

98 Scopus citations


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.

Original languageEnglish (US)
Article number8793143
Pages (from-to)4714-4727
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number12
StatePublished - Dec 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


  • Ride-sharing
  • deep Q-network (DQN)
  • distributed algorithm
  • road network
  • vehicle dispatch


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