Deep Reinforcement Learning Based Traffic Signal Control: A Comparative Analysis-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning Based Traffic Signal Control: A Comparative Analysis

Published:2023 Issue: Volume:220 Page:275-282
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Wu Chunliang,Kim Inhi,Ma Zhenliang

Funder

National Research Foundation of Korea

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Subject

General Engineering

Reference16 articles.

1. National Transportation Operations Coalition. (2012). National traffic signal report card.

2. arXiv preprint;Wei,2019

3. Deep reinforcement learning: A brief survey;Arulkumaran;IEEE Signal Processing Magazine,2017

4. Design of reinforcement learning parameters for seamless application of adaptive traffic signal control;El-Tantawy;Journal of Intelligent Transportation Systems,2014

5. Intellilight: A reinforcement learning approach for intelligent traffic light control;Wei,2018

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1. Urban traffic signal control optimization through Deep Q Learning and double Deep Q Learning: a novel approach for efficient traffic management;Multimedia Tools and Applications;2024-08-27

2. Adaptive urban traffic signal control based on enhanced deep reinforcement learning;Scientific Reports;2024-06-19

3. Unscrambling traffic congestion and increasing sustainability in special urban intersection;E3S Web of Conferences;2024

4. A Bus Signal Priority Control Method Based on Deep Reinforcement Learning;Applied Sciences;2023-06-02

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