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Open AccessArticle
Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization
by
Gongquan Zhang
Gongquan Zhang 1,2,
Fangrong Chang
Fangrong Chang 3,*,
Helai Huang
Helai Huang
Helai Huang is a Senior Professor of transportation engineering at the School of Traffic and at He a [...]
Helai Huang is a Senior Professor of transportation engineering at the School of Traffic and Transportation Engineering at Central South University (CSU). He is the founding director of the Smart Transport Laboratory of Hunan Province and the dean of the CSU School of International Education. He received his B.E and M.S. degrees from Tianjin University in 2000 and 2003, respectively, and earned a Ph.D. degree in transportation engineering from the National University of Singapore in 2008, before working as a post-doc at the University of Central Florida (2008–2010). He is the vice-director of the International Collaboration Committee, Chinese Society of Engineers (CSE), and committee chair of Transport Safety and Environment, World Transport Convention (WTC). He is listed in the Clarivate highly cited Researchers, top 2% scientists worldwide by Stanford University, and China Leading Talents of Technological Innovation of the Ten-Thousand Talents Program. He is the winner of the Outstanding Talents Award in China ITS. His research interests include traffic safety, transportation planning, and ITS.
1 and
Zilong Zhou
Zilong Zhou
Zilong Zhou is a Professor at the School of Resources & Safety Engineering at Central South He is of [...]
Zilong Zhou is a Professor at the School of Resources & Safety Engineering at Central South University. He is also the vice dean of the school and currently serves as the committee member of the Underground Space Sub-Society of the Chinese Society for Rock Mechanics and Engineering (CSRME), commissioned in Rock Dynamics of the International Society for Rock Mechanics (ISRM). He received his Bachelor’s and Ph.D. degrees in Mining and Geotechnical Engineering from Central South University in 2002 and 2007, respectively. He joined the CSU faculty in 2004, and had co-research experience with Nanyang Technological University (NTU, Singapore), the University of Toronto (Canada), and Jiangxi Copper Corporation. His research interests are focused on the hazard prevention of mining and geotechnical engineering.
3
1
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
2
Harvard Medical School, Harvard University, Boston, MA 02138, USA
3
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Mathematics 2024, 12(13), 2056; https://doi.org/10.3390/math12132056 (registering DOI)
Submission received: 21 May 2024
/
Revised: 19 June 2024
/
Accepted: 27 June 2024
/
Published: 30 June 2024
Abstract
To improve traffic efficiency, adaptive traffic signal control (ATSC) systems have been widely developed. However, few studies have proactively optimized the air environmental issues in the development of ATSC. To fill this research gap, this study proposes an optimized ATSC algorithm to take into consideration both traffic efficiency and decarbonization. The proposed algorithm is developed based on the deep reinforcement learning (DRL) framework with dual goals (DRL-DG) for traffic control system optimization. A novel network structure combining Convolutional Neural Networks and Long Short-Term Memory Networks is designed to map the intersection traffic state to a Q-value, accelerating the learning process. The reward mechanism involves a multi-objective optimization function, employing the entropy weight method to balance the weights among dual goals. Based on a representative intersection in Changsha, Hunan Province, China, a simulated intersection scenario is constructed to train and test the proposed algorithm. The result shows that the ATSC system optimized by the proposed DRL-DG results in a reduction of more than 71% in vehicle waiting time and 46% in carbon emissions compared to traditional traffic signal control systems. It converges faster and achieves a balanced dual-objective optimization compared to the prevailing DRL-based ATSC.
Share and Cite
MDPI and ACS Style
Zhang, G.; Chang, F.; Huang, H.; Zhou, Z.
Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization. Mathematics 2024, 12, 2056.
https://doi.org/10.3390/math12132056
AMA Style
Zhang G, Chang F, Huang H, Zhou Z.
Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization. Mathematics. 2024; 12(13):2056.
https://doi.org/10.3390/math12132056
Chicago/Turabian Style
Zhang, Gongquan, Fangrong Chang, Helai Huang, and Zilong Zhou.
2024. "Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization" Mathematics 12, no. 13: 2056.
https://doi.org/10.3390/math12132056
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