Back to Search
Start Over
Integrated operations strategies for shared and privately-owned autonomous vehicles: A deep reinforcement learning framework.
- Source :
-
Transportation Research Part C: Emerging Technologies . Jun2024, Vol. 163, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- The shared autonomous vehicle (SAV), combining shared mobility and vehicle automation, will play a pivotal role in urban transportation. Effectively matching SAVs to stochastic customer demand poses a significant challenge, particularly when addressing microscopic multi-AV coordination to prevent conflicts and congestion within a network. To tackle this issue, we propose a novel traffic control scheme called the Integrated Operation Framework for Order Matching and AV Coordination (I-OFOMAC). This scheme introduces a third-party coordinator responsible for integrating online order matching for the SAV platform and real-time AV coordination for the road manager. Our approach utilizes an offline deep reinforcement learning method to train a state value neural network, which facilitates strategic order matching. Additionally, a neural network approximating bisimulation metrics is trained to enhance exploration and accelerate learning. To achieve scalability in network-level AV coordination, we adopt a scheduled transportation scheme. The coordinator solves the I-OFOMAC during each decision epoch, optimizing order matching and AV coordination. The I-OFOMAC aims to balance the goals of minimizing AV travel delay and maximizing SAV service revenue. We conduct extensive simulation studies in the realistic Wangjing network to evaluate the proposed I-OFOMAC scheme. Compared to two-step benchmark frameworks that implement order matching and AV coordination sequentially, the I-OFOMAC achieves an average increase of 13.28% in platform revenue (ranging from 2% to 22.53%) and a 4.48% reduction in average PAV travel delay (ranging from −6.55% to 20.17%). • Mixed AV zone. • Online operations of joint order matching and AV coordination. • Offline deep reinforcement learning framework. • Bisimulation-guided exploration. • A scalable approximate algorithm. • Numerical studies in realistic Wangjing network. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0968090X
- Volume :
- 163
- Database :
- Academic Search Index
- Journal :
- Transportation Research Part C: Emerging Technologies
- Publication Type :
- Academic Journal
- Accession number :
- 177485029
- Full Text :
- https://doi.org/10.1016/j.trc.2024.104621