Advanced State-Aware Traffic Light Optimization Control with Deep Q-Network
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8082-6_14
Reference18 articles.
1. Xu, L., Kun, W., Pengfei, L., Miaoyu, X.: Deep learning short-term traffic flow prediction based on lane changing behavior recognition. In: 2020 2nd International Conference on Information Technology and Computer Application (ITCA), pp. 760–763 (2020). https://doi.org/10.1109/ITCA52113.2020.00163
2. Tran-Van, N.Y., Nguyerr, X.H., Le, K.H.: Towards smart traffic lights based on deep learning and traffic flow information. In: 2022 9th NAFOSTED Conference on Information and Computer Science (NICS), pp. 1–6 (2022). https://doi.org/10.1109/NICS56915.2022.10013375
3. Maduka, N.C., Ajibade, I.I., Bello, M.: Modelling and optimization of smart traffic light control system. In: 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), pp. 1–5 (2022). https://doi.org/10.1109/NIGERCON54645.2022.9803073
4. Abhishek, A., Nayak, P., Hegde, K.P., Lakshmi Prasad, A., Nagegowda, K.S.: Smart traffic light controller using deep reinforcement learning. In: 2022 3rd International Conference for Emerging Technology (INCET), pp. 1–5 (2022). https://doi.org/10.1109/INCET54531.2022.9824501
5. Raeisi, M., Mahboob, A.S.: Intelligent control of urban intersection traffic light based on reinforcement learning algorithm. In: 2021 26th International Computer Conference, Computer Society of Iran (CSICC), pp. 1–5 (2021). https://doi.org/10.1109/CSICC52343.2021.9420622
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