Version 1
: Received: 3 October 2023 / Approved: 3 October 2023 / Online: 4 October 2023 (11:16:19 CEST)
How to cite:
Oh, Y. SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning. Preprints2023, 2023100191. https://doi.org/10.20944/preprints202310.0191.v1
Oh, Y. SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning. Preprints 2023, 2023100191. https://doi.org/10.20944/preprints202310.0191.v1
Oh, Y. SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning. Preprints2023, 2023100191. https://doi.org/10.20944/preprints202310.0191.v1
APA Style
Oh, Y. (2023). SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning. Preprints. https://doi.org/10.20944/preprints202310.0191.v1
Chicago/Turabian Style
Oh, Y. 2023 "SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning" Preprints. https://doi.org/10.20944/preprints202310.0191.v1
Abstract
Recently, smart mobility intelligent traffic services have become a critical task in Intelligent Transportation Systems (ITS). This involves not only the use of advanced sensors and controllers but also the ability to respond to real-time traffic situations at intersections, alleviate congestion, and generate policies to prevent traffic jams. DRL (Deep Reinforcement Learning) provides a natural framework for processing tasks. In DRL, each intersection can control itself and coordinate with neighbors to achieve optimal network-wide policies. However, comparing approaches remains a challenging task due to the existence of numerous possible configurations. This research performs a critical comparison of various traffic controllers found in the literature. It demonstrates that using a nonlinear approximator for coordination mechanisms and enhancing observability at each intersection are key performance drivers.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
