S-Edge: heterogeneity-aware, light-weighted, and edge computing integrated adaptive traffic light control framework-Reference-Cited by-同舟云学术

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S-Edge: heterogeneity-aware, light-weighted, and edge computing integrated adaptive traffic light control framework

Published:2023-04-12 Issue:13 Volume:79 Page:14923-14953
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Sachan Anuj,Kumar Neetesh

Funder

Department of Science and Technology (DST), ICPS Division, New Delhi, Government of India

Council of Scientific and Industrial Research (CSIR), New Delhi, Government of India

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-023-05216-0.pdf

Reference49 articles.

1. Abdulhai B, Pringle R, Karakoulas GJ (2003) Reinforcement learning for true adaptive traffic signal control. J Transp Eng 129(3):278–285

2. Albagul A, Hrairi M, Hidayathullah M et al (2006) Design and development of sensor based traffic light system. Am J Appl Sci 3(3):1745–1749

3. An J, Hu M, Fu L, Zhan J (2019) A novel fuzzy approach for combining uncertain conflict evidences in the dempster-shafer theory. IEEE Access 7:7481–7501

4. Andrenacci N, Genovese A, Ragona R (2017) Determination of the level of service and customer crowding for electric charging stations through fuzzy models and simulation techniques. Appl Energy 208:97–107

5. Bai Y, Zhuang H, Wang D (2006) Advanced fuzzy logic technologies in industrial applications. Springer, Berlin

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2. SDN-enabled Quantized LQR for Smart Traffic Light Controller to Optimize Congestion;ACM Transactions on Internet Technology;2024-02-22

3. A Survey on Adaptive Smart Urban Systems;IEEE Access;2024

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