Dynamic network congestion pricing based on deep reinforcement learning
- URL: http://arxiv.org/abs/2206.12188v1
- Date: Fri, 24 Jun 2022 10:00:20 GMT
- Title: Dynamic network congestion pricing based on deep reinforcement learning
- Authors: Kimihiro Sato, Toru Seo, Takashi Fuse
- Abstract summary: This work proposes a dynamic congestion pricing method using deep reinforcement learning (DRL)
It is designed to eliminate traffic congestion based on observable data in general large-scale road networks.
One of the novel elements of the proposed method is the distributed and cooperative learning scheme.
- Score: 0.5801044612920815
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Traffic congestion is a serious problem in urban areas. Dynamic congestion
pricing is one of the useful schemes to eliminate traffic congestion in
strategic scale. However, in the reality, an optimal dynamic congestion pricing
is very difficult or impossible to determine theoretically, because road
networks are usually large and complicated, and behavior of road users is
uncertain. To account for this challenge, this work proposes a dynamic
congestion pricing method using deep reinforcement learning (DRL). It is
designed to eliminate traffic congestion based on observable data in general
large-scale road networks, by leveraging the data-driven nature of deep
reinforcement learning. One of the novel elements of the proposed method is the
distributed and cooperative learning scheme. Specifically, the DRL is
implemented by a spatial-temporally distributed manner, and cooperation among
DRL agents is established by novel techniques we call spatially shared reward
and temporally switching learning. It enables fast and computationally
efficient learning in large-scale networks. The numerical experiments using
Sioux Falls Network showed that the proposed method works well thanks to the
novel learning scheme.
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