Reducing Urban Traffic Congestion Using Deep Learning and Model Predictive Control
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, New York University, Brooklyn, NY, USA
2. Buildings and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Funder
U
National Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software
Link
http://xplorestaging.ieee.org/ielx7/5962385/6104215/10101858.pdf?arnumber=10101858
Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Traffic Signal Control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-based Sensors;2024 IEEE 18th International Conference on Control & Automation (ICCA);2024-06-18
2. A Real-Time Demand Management and Route-Guidance System for Eliminating Congestion;IEEE Intelligent Transportation Systems Magazine;2024
3. Learning Multi-Intersection Traffic Signal Control via Coevolutionary Multi-Agent Reinforcement Learning;IEEE Transactions on Intelligent Transportation Systems;2024
4. AI-Powered Traffic Orchestration: Reinforcement Learning at Traffic Intersections;Lecture Notes in Electrical Engineering;2024
5. A Congestion Sub-area Coordinated Control Model for Queue Suppression Based on Improved Evolutionary Algorithm;2023 3rd International Conference on Robotics, Automation and Intelligent Control (ICRAIC);2023-11-24
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