Reinforcement learning based traffic signal controller with state reduction

Alharthi, 2017, Data visualization to explore improving decision-making within hajj services, Sci. Model. Res., 2, 9 Aly, 2014, Information gathering schemes for collaborative sensor devices, Procedia Comput. Sci., 32, 1141, 10.1016/j.procs.2014.05.545 Arel, 2010, Reinforcement learning-based multi-agent system for network traffic signal control, IET Intell. Transp. Syst., 4, 128, 10.1049/iet-its.2009.0070 Ashwini, 2020, Data sources for urban traffic prediction: a review on classification, comparison and technologies, Proc. 3rd Int. Conf. Intell. Sustain. Syst., 2020, 628 Aslani, 2018, Traffic signal optimization through discrete and continuous reinforcement learning with robustness analysis in downtown Tehran, Adv. Eng. Inform., 38, 639, 10.1016/j.aei.2018.08.002 B P, 2022, Artificial intelligence in smart city applications: an overview. 6th international conference on intelligent computing and control systems (ICICCS), ICICCS, 986 Badage, 2018, IRJET-V5I9158-with-cover-page-v2, Int. Res. J. Eng. Technol., 5, 866 Casas, 2017, Deep deterministic policy gradient for urban traffic light, Control. ArXiv Prepr., 1703, 09035 Chen, 2020, Toward a thousand lights: decentralized deep reinforcement learning for large-scale traffic signal control, AAAI 2020 - 34th AAAI Conf. Artif. Intell., 3414 Chu, 2019, Multi-agent deep reinforcement learning for large-scale traffic signal control, IEEE Trans. Intell. Transp. Syst., 21, 1086, 10.1109/TITS.2019.2901791 Curtis, 2013, Right of way: asymmetric agent interactions in crowds, Vis. Comput., 29, 1277, 10.1007/s00371-012-0769-x Das, 2021, Power quality improvement using fuzzy logic-based compensation in a hybrid power system, Int. J. Power Electron. Drive Syst., 12, 576 De Oliveira, 2006, Reinforcement learning-based control of traffic lights in non-stationary environments: a case study in a microscopic simulator, CEUR Workshop Proc., 223 El-Tantawy, 2014, Design of reinforcement learning parameters for seamless application of adaptive traffic signal control, J. Intell. Transp. Syst.: Technol. Plan. Oper., 18, 227, 10.1080/15472450.2013.810991 Grégoire, 2007, Urban traffic control based on learning agents, IEEE Conf. Intell. Transp. Syst. Proc. ITSC, 916 Jun-Qiang, 2012, Travel time model of left-turning vehicles at signalized intersection, Math. Probl. Eng., 2012 Kaysi, 2013, Users’ evaluation of rail systems in mass events, Transp. Res. Rec., 2350, 111, 10.3141/2350-13 Kim, 2015, Velocity-based modeling of physical interactions in dense crowds, Vis. Comput., 31, 541, 10.1007/s00371-014-0946-1 Kuang, 2021, Intelligent traffic signal control based on reinforcement learning with state reduction for smart cities, ACM Trans. Internet Technol., 21, 10.1145/3418682 Liang, 2019, Deep reinforcement learning for traffic light control in vehicular networks, IEEE Trans. Veh. Technol., 68, 1243, 10.1109/TVT.2018.2890726 Ma, 2021, Adaptive optimization of traffic signal timing via deep reinforcement learning, J. Adv. Transp., 2021 Meghana, 2022, Blockchain technology based decentralized energy management in multi-microgrids including electric vehicles, J. Intell. Fuzzy Syst., 42, 991, 10.3233/JIFS-189766 Panda, 2022, Prediction of diabetes disease using machine learning algorithms, IAES Int. J. Artif. Intell., 11, 284 R M, 2022, Variability analysis of isolated intersections through case study, Int. J. Eng. Trends Technol., 70, 359, 10.14445/22315381/IJETT-V70I8P237 Reddy, 2018, Multi-objective optimization considering cost, emission and loss objectives using PSO and fuzzy approach, Int. J. Eng. Technol. (UAE), 7, 1552, 10.14419/ijet.v7i3.11203 Salkham, 2008, A collaborative reinforcement learning approach to urban traffic control optimization, Proc. - 2008 IEEE/WIC/ACM Int. Conf. Intell. Agent Technol., IAT, 2008, 560, 10.1109/WIIAT.2008.88 Savithramma, R.M., Ashwini, B.P., & Sumathi, R. 2022. Smart Mobility Implementation in Smart Cities: A Comprehensive Review on State-of-art Technologies. 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), 10–17. doi.org/10.1109/icssit53264.2022.9716288. Savithramma, 2020, Road traffic signal control and management system: a survey, Proc. 3rd Int. Conf. Intell. Sustain. Syst., 104 Savithramma, 2022, A comparative analysis of machine learning algorithms in design process of adaptive traffic signal control system, J. Phys. Conf. Ser., 2161, 10.1088/1742-6596/2161/1/012054 Savithramma, 2022, Comparative analysis of waiting delay estimation models for signalized intersections: a case study Tumakuru city, Proc. Third Int. Conf. Inf. Manag. Mach. Intell. Viti, 2010, Probabilistic models for queues at fixed control signals, Transp. Res. Part B: Methodol., 44, 120, 10.1016/j.trb.2009.05.001 Zhang, 2021, Using reinforcement learning with partial vehicle detection for intelligent traffic signal control, IEEE Trans. Intell. Transp. Syst., 22, 404, 10.1109/TITS.2019.2958859 Zhao, 2019, Various methods for queue length and traffic volume estimation using probe vehicle trajectories, Transp. Res. Part C: Emerg. Technol., 107, 70, 10.1016/j.trc.2019.07.008