Adaptive Frequency Green Light Optimal Speed Advisory Based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Adaptive Frequency Green Light Optimal Speed Advisory Based on Deep Reinforcement Learning

Published:2024-10 Issue:10 Volume:150 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Xu Ming¹^ORCID,Zuo Dongyu²^ORCID,Li Jinye³

Affiliation:

1. Professor, Dept. of Software College, Liaoning Technical Univ., Huludao 125105, China. ORCID: .

2. Researcher, Dept. of Software College, Liaoning Technical Univ., Huludao 125105, China (corresponding author). ORCID: .

3. Researcher, Dept. of Software College, Liaoning Technical Univ., Huludao 125105, China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-8392

Reference26 articles.

1. Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time

2. Hybrid reinforcement learning-based eco-driving strategy for connected and automated vehicles at signalized intersections;Bai Z.;IEEE Trans. Intell. Transp. Syst.,2022

3. Erdmann J. 2013. “Combining adaptive junction control with simultaneous green-light-optimal-speed-advisory.” In Proc. 2013 IEEE 5th Int. Symp. on Wireless Vehicular Communications (WiVeC) 1–5. New York: IEEE.

4. Fan Z. R. Su W. Zhang and Y. Yu. 2019. “Hybrid actor-critic reinforcement learning in parameterized action space.” Preprint submitted March 4 2019. https://arxiv.org/abs/1903.01344.

5. Eco-driving technology for sustainable road transport: A review;Huang Y.;Renewable Sustainable Energy Rev.,2018

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