Addressing Urban Traffic Congestion: A Hybrid DQN-Autoencoder Model with HyperOPT Tuning-Reference-Cited by-同舟云学术

Addressing Urban Traffic Congestion: A Hybrid DQN-Autoencoder Model with HyperOPT Tuning

Published:2024-06-13 Issue: Volume:17 Page:739-749
ISSN:
Container-title:International Conference on Transportation and Development 2024
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Author:

Balakrishnan Anurag¹¹²³,Pathak Satyam¹¹²³,Herrera Pedro¹¹²³,Liu Tairan¹¹²³

Affiliation:

1. M.S. Student, Dept. of Mechanical and Aerospace Engineering, California State Univ. Long Beach, Long Beach, CA.

2. Undergraduate Student, Dept. of Mechanical and Aerospace Engineering, California State Univ. Long Beach, Long Beach, CA.

3. Perception, Actuation, Control, and Network (PACK) Laboratory, Dept. of Mechanical and Aerospace Engineering, California State Univ. Long Beach, Long Beach, CA.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485521.066

Reference25 articles.

1. Baldi, S., Michailidis, I., Ntampasi, V., Kosmatopoulos, E. B., Papamichail, I., and Papageorgiou, M. (2015). Simulation-based synthesis for approximately optimal urban traffic light management. In 2015 American Control Conference (ACC) (pp. 868–873). IEEE.

2. A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches;Djahel S.;IEEE Communications Surveys & Tutorials,2014

3. Gao J. Shen Y. Liu J. Ito M. and Shiratori N. (2017). Adaptive traffic signal control: Deep reinforcement learning algorithm with experience replay and target network. arXiv preprint arXiv:1705.02755.

4. Cooperative Multi-Intersection Traffic Signal Control Based on Deep Reinforcement Learning

5. Huo, Y., Hu, J., Wang, G., and Chen, J. (2018). A traffic signal control method based on asynchronous reinforcement learning. In 18th COTA International Conference of Transportation Professionals (pp. 1444–1453). Reston, VA: American Society of Civil Engineers.