Open AccessRebalancing Autonomous Vehicles using Deep Reinforcement Learning
Author(s) -
Jiajie Dai,
Qianyu Zhu,
Nan Jiang,
Wuyang Wang
Publication year - 2022
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
ISSN - 1998-4464
DOI - 10.46300/9106.2022.16.80
Subject(s) - reinforcement learning , idle , supply and demand , computer science , on demand , mile , mode (computer interface) , operations research , transport engineering , artificial intelligence , engineering , economics , microeconomics , human–computer interaction , multimedia , physics , astronomy , operating system
The shared autonomous mobility-on-demand (AMoD) system is a promising business model in the coming future which provides a more efficient and affordable urban travel mode. However, to maintain the efficient operation of AMoD and address the demand and supply mismatching, a good rebalancing strategy is required. This paper proposes a reinforcement learning-based rebalancing strategy to minimize passengers’ waiting in a shared AMoD system. The state is defined as the nearby supply and demand information of a vehicle. The action is defined as moving to a nearby area with eight different directions or staying idle. A 4.6 4.4 km2 region in Cambridge, Massachusetts, is used as the case study. We trained and tested the rebalancing strategy in two different demand patterns: random and first-mile. Results show the proposed method can reduce passenger’s waiting time by 7% for random demand patterns and 10% for first-mile demand patterns.