Problem-Specific Knowledge Based Multi-Objective Meta-Heuristics Combined Q-Learning for Scheduling Urban Traffic Lights With Carbon Emissions

Lin, Zhongjie; Gao, Kaizhou; Wu, Naiqi; Nagaratnam Suganthan, Ponnuthurai

doi:10.1109/tits.2024.3397077

IEEE Trans. Intell. Transport. Syst.

2024

DOI: 10.1109/tits.2024.3397077

|View full text |Cite

Problem-Specific Knowledge Based Multi-Objective Meta-Heuristics Combined Q-Learning for Scheduling Urban Traffic Lights With Carbon Emissions

Zhongjie Lin,

Kaizhou Gao,

Naiqi Wu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Modelling and scheduling distributed assembly permutation flow-shops using reinforcement learning-based evolutionary algorithms

Qiu,

Gao,

et al. 2025

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Modelling and scheduling distributed assembly permutation flow-shops using reinforcement learning-based evolutionary algorithms

Qiu,

Gao,

et al. 2025

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization

Zhang,

Chang,

Huang

et al. 2024

Mathematics

View full text Add to dashboard Cite

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.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Problem-Specific Knowledge Based Multi-Objective Meta-Heuristics Combined Q-Learning for Scheduling Urban Traffic Lights With Carbon Emissions

Cited by 2 publications

References 46 publications

Modelling and scheduling distributed assembly permutation flow-shops using reinforcement learning-based evolutionary algorithms

Modelling and scheduling distributed assembly permutation flow-shops using reinforcement learning-based evolutionary algorithms

Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization

Contact Info

Product

Resources

About