Optimizing traffic efficiency via a reinforcement learning approach based on time allocation-Reference-Cited by-同舟云学术

Optimizing traffic efficiency via a reinforcement learning approach based on time allocation

Published:2023-04-25 Issue:10 Volume:14 Page:3381-3391
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Xiang Chao,Jin Zhongming,Yu Zhengxu,Hua Xian-Sheng,Hu Yao,Qian Wei,Zhu Kaili,Cai Deng,He Xiaofei

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01838-1.pdf

Reference34 articles.

1. Schneider B (2018) Traffic’s mind-boggling economic toll. Accessed 7 February 2018. https://www.citylab.com/transportation/2018/02/traffics-mind-boggling-economic-toll/552488/

2. Burfeind M (2018) Traffic congestion cost UK motorists over £$$37.7$$ billion in 2017. Accessed 5 Feb 2018. http://inrix.com/press-releases/scorecard-2017-uk/

3. Smith SF, Barlow G, Xie X-F, Rubinstein ZB (2013) Surtrac: scalable urban traffic control

4. Chin S-M, Franzese O, Greene DL, Hwang H-L, Gibson R et al (2004) Temporary losses of highway capacity and impacts on performance: Phase 2. United States. Dept. of Energy. Office of Scientific and Technical Information

5. Webster FV (1958) Traffic signal settings. Technical report

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