A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem

Erotokritos Skordilis, Yi Hou, Charles Tripp, Matthew Moniot, Peter Graf, David Biagioni

Research output: Contribution to journalArticlepeer-review

3 Scopus Citations

Abstract

Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing. For this reason, operators tend to employ simplified algorithms that have been demonstrated to work well in a particular setting. To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost. In particular, by treating dispatch as part of the environment dynamics, a centralized agent can learn to intermittently direct the dispatcher to reposition free vehicles and mitigate against fleet imbalance. We formulate RL state and action spaces as distributions over a grid partitioning of the operating area, making the framework scalable and avoiding the complexities associated with multiagent RL. Numerical experiments, using real-world trip and network data, demonstrate that RL reduces waiting time by 28% to 38% for the same-day evaluation, 17% to 44% for cross-day evaluation, and 22% to 25% for cross-season evaluation compared with no rebalancing scenarios. This approach has several distinct advantages over baseline methods including: improved system cost; high degree of adaptability to the selected dispatch method; and the ability to perform scale-invariant transfer learning between problem instances with similar vehicle and request distributions.

Original languageAmerican English
Pages (from-to)11903-11916
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Volume23
Issue number8
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.

NREL Publication Number

  • NREL/JA-2C00-78840

Keywords

  • Fleet management
  • Intelligent control
  • Mobility on demand
  • Optimal rebalancing
  • Reinforcement learning

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