- مهماندار، م.، آریانا، م.، مبادری، ت. و خلیلی، ا. (1399) “ ارزیابی مولفه های موثر بر ارتقای فرهنگ ایمنی ترافیک و کاهش تلفات با موتورسیکلت”، فصلنامه مهندسی حمل و نقل، سال یازدهم، شماره سوم، ص.649-663.
- Bálint, K., Tamás, T. and Tamás, B. (2022) “Deep Reinforcement Learning based approach for Traffic Signal Control”. Transportation Research Procedia. Vol. 62, pp. 278–285.
- Casas N. (2017) “Deep deterministic policy gradient for urban traffic light control”. arXiv preprint arXiv:170309035.
- Chu, K.F., Lam, A.Y. and Li, V.O. (2021) “Traffic Signal Control Using End-to-End Off-Policy Deep Reinforcement Learning”. IEEE Transactions on Intelligent Transportation Systems.
- Chu, T., Wang, J., Codecà, L. and Li, Z. (2019) “Multi-agent deep reinforcement learning for large-scale traffic signal control”. IEEE Transactions on Intelligent Transportation Systems. Vol. 21, No. 3, pp. 1086–1095.
- Essa, M. and Sayed, T. (2020) “Self-learning adaptive traffic signal control for real-time safety optimization”. Accident Analysis & Prevention. Vol. 146:105713.
- 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:170502755.
- Genders, W. and Razavi., S. (2016) “Using a deep reinforcement learning agent for traffic signal control”. arXiv preprint arXiv:161101142.
- Gong, Y., Abdel-Aty, M., Yuan, J. and Cai, Q. (2020) “Multi-objective reinforcement learning approach for improving safety at intersections with adaptive traffic signal control”. Accident Analysis & Prevention. Vol. 144:105655.
- Jamil. ARM., Ganguly. KK. and Nower. N (2021) “Adaptive traffic signal control system using composite reward architecture based deep reinforcement learning”. IET Intelligent Transport Systems. Vol. 14, No. 14, pp. 2030–2041.
- Krajzewicz, D., Erdmann, J., Behrisch, M. and Bieker, L. (2012) “Recent development and applications of SUMO-Simulation of Urban MObility”. International journal on advances in systems and measurements. Vol. 5(3 & 4).
- Li, L., Lv, Y. and Wang, F.Y. (2016) “Traffic signal timing via deep reinforcement learning”. IEEE/CAA Journal of Automatica Sinica. Vol. 3, No.3, pp. 247–254.
- Li, M., Li, Z., Xu, C. and Liu, T. (2020) “Deep reinforcement learning-based vehicle driving strategy to reduce crash risks in traffic oscillations. Transportation research record”. Vol. 2674, No. 10, pp. 42–54.
- Liang, X., Du, X., Wang, G. and Han, Z. (2018) “Deep reinforcement learning for traffic light control in vehicular networks”. arXiv preprint arXiv:180311115.
- Liang, X., Du, X., Wang, G and Han, Z. (2019) “A deep q learning network for traffic lights’ cycle control in vehicular networks”. IEEE Transactions on Vehicular Technology. Vol. 68, No. 2, pp. 1243–1253.
- Maurya, A.K., Dey, S. and Das, S. (2015) “Speed and time headway distribution under mixed traffic condition”. Journal of the Eastern Asia Society for Transportation Studies. Vol, 11. pp. 1774–1792.
- Mousavi, S.S., Schukat M. and Howley, E. (2017) “Traffic light control using deep policy-gradient and value-function-based reinforcement learning”. IET Intelligent Transport Systems. Vol. 11, No. 7, pp. 417–423.
- Paul, A. and Mitra, S. (2022) “Exploring reward efficacy in traffic management using deep reinforcement learning in intelligent transportation system”. ETRI Journal. Vol. 44, No. 2, pp. 194–207.
- Rodrigues, F. and Azevedo, C.L. (2019) “Towards robust deep reinforcement learning for traffic signal control: Demand surges, incidents and sensor failures”. IEEE intelligent transportation systems conference (ITSC). Auckland, New Zealand: 27-30 October 2019.
- Sutton, R.S.and Barto, A.G. (2018) “Reinforcement learning: An introduction”. MIT press.
- Van der Pol, E., Oliehoek, F.A. (2016) “Coordinated deep reinforcement learners for traffic light control”. Proceedings of Learning, Inference and Control of Multi-Agent Systems (NIPS).
- Vidali, A., Crociani, L., Vizzari, G. and Bandini, S. (2019) “A Deep Reinforcement Learning Approach to Adaptive Traffic Lights Management”. In: WOA. Parma, Italy, 26-28 June 2019.
- Wang. T., Cao. J. and. Hussain A (2021) “Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning”. Transportation research part C: emerging technologies. Vol. 125:103046.
- Wei. H., Zheng, G., Gayah, V. and Li, Z. (2021) “Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation”. ACM SIGKDD Explorations Newsletter. Vol. 22, No. 2, pp. 12–18.
- Wei, H., Zheng, G., Yao, H. and Li, Z. (2018) “Intellilight: A reinforcement learning approach for intelligent traffic light control”. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 2496–2505.
- Yoon, J., Ahn, K., Park, J and Yeo, H. (2021) “Transferable traffic signal control: Reinforcement learning with graph centric state representation”. Transportation Research Part C: Emerging Technologies. Vol. 130:103321.
- Roy, A., Hossain, M. and Muromachi, Y. (2022) “A deep reinforcement learning-based intelligent intervention framework for real-time proactive road safety management”. Accident Analysis & Prevention. Vol. 165, pp. 106512.
- Zheng, G., Zang, X., Xu, N., Wei, H., Yu, Z., Gayah, V., Xu, K. and Li Z. (2019) “Diagnosing reinforcement learning for traffic signal control”. arXiv preprint arXiv:190504716.