Author:
Deepika ,Pandove Gitanjali
Publisher
Springer Science and Business Media LLC
Reference49 articles.
1. Xiao, Z., Xiao, Z., Wang, D., & Li, X. (2015). An intelligent traffic light control approach for reducing vehicles CO2 emissions in VANET. In The 12th international conference on fuzzy systems and knowledge discovery (FSKD) (pp. 2070–2075).
2. Gahlan, D., & Pandove, G. (2020). A review on various issues, challenges and different methodologies in vehicular environment. In International conference on innovative computing & communications (ICICC) 2020. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3606267.
3. Natafgi, M. B., Osman, M., Haidar, A. S., & Hamandi, L. (2018). Smart traffic light system using machine learning. In IEEE international multidisciplinary conference on engineering technology (IMCET) (pp. 1–6).
4. Navarro-Espinoza, A., López-Bonilla, O. R., García-Guerrero, E. E., Tlelo-Cuautle, E., López-Mancilla, D., Hernández-Mejía, C., & Inzunza-González, E. (2022). Traffic flow prediction for smart traffic lights using machine learning algorithms. Technologies, 10, 5.
5. Paul, A., Haricharan, J., & Mitra, S. (2022). An intelligent traffic signal management strategy to reduce vehicles CO2 emissions in fog oriented VANET. Wireless Personal Communications, 122, 543–576.
Cited by
1 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