| [1] |
Mirchandani P, Head L. 2001. A real-time traffic signal control system: architecture, algorithms, and analysis. |
| [2] |
Rasheed F, Yau KA, Noor RM, Wu C, Low YC. 2020. Deep reinforcement learning for traffic signal control: a review. |
| [3] |
Qin Z, Ji A, Sun Z, Wu G, Hao P, et al. 2024. Game theoretic application to intersection management: a literature review. |
| [4] |
Maslekar N, Mouzna J, Boussedjra M, Labiod H. 2013. CATS: an adaptive traffic signal system based on car-to-car communication. |
| [5] |
Hoogendoorn S, Knoop V. 2013. Traffic flow theory and modelling. In The transport system and transport policy: An introduction, eds. van Wee B, Annema JA, Banister D, Pudāne B. Cheltenham, UK: Edward Elgar Publishing. pp. 125–59 |
| [6] |
Miller A. 1963. A computer control system for traffic networks. Proceedings of the International Symposium on the Theory of Traffic Flow and Transportation, London, UK, 1963. London, UK: Organisation for Economic Co-operation and Development. https://trid.trb.org/View/612653 |
| [7] |
Zheng X, Recker W, Chu L. 2010. Optimization of control parameters for adaptive traffic-actuated signal control. |
| [8] |
Zheng X, Chu L. 2008. Optimal parameter settings for adaptive traffic-actuated signal control. 2008 11th International IEEE Conference on Intelligent Transportation Systems. October 12-15, 2008, Beijing, China. USA: IEEE. pp. 105−10. doi: 10.1109/ITSC.2008.4732676 |
| [9] |
Sims AG, Dobinson KW. 1980. The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits. |
| [10] |
Gartner N. 1983. OPAC: A demand-responsive strategy for traffic signal control. Transportation Research Record, No. 906. pp. 75–81 |
| [11] |
Bing B, Carter A. 1995. SCOOT: The world's foremost adaptive TRAFFIC control system. Traffic Technology International '95. Surrey, UK: UK and International Press. https://trid.trb.org/View/415757 |
| [12] |
Henry JJ, Farges JL, Tuffal J. 1984. The PRODYN real time traffic algorithm. In Control in Transportation Systems. Proceedings of the 4th IFAC/IFIP/IFORS Conference, Baden-Baden, Federal Republic of Germany, 20–22 April 1983. Germany: Elsevier. pp. 305–10. doi: 10.1016/B978-0-08-029365-3.50048-1 |
| [13] |
Brilon W, Wietholt T. 2013. Experiences with adaptive signal control in Germany. |
| [14] |
Lertworawanich P, Unhasut P. 2021. A CO emission-based adaptive signal control for isolated intersections. |
| [15] |
Mondal MA, Rehena Z. 2022. Priority-based adaptive traffic signal control system for smart cities. |
| [16] |
Lee WH, Wang HC. 2022. A person-based adaptive traffic signal control method with cooperative transit signal priority. |
| [17] |
Jing P, Huang H, Chen L. 2017. An adaptive traffic signal control in a connected vehicle environment: a systematic review. |
| [18] |
Liu Z. 2007. A survey of intelligence methods in urban traffic signal control. International Journal of Computer Science and Network Security 7(7):105−12 |
| [19] |
Mannion P, Duggan J, Howley E. 2016. An experimental review of reinforcement learning algorithms for adaptive traffic signal control. In Autonomic Road Transport Support Systems, edds. McCluskey T, Kotsialos A, Müller J, Klügl F, Rana O, et al. Cham: Springer International Publishing. pp. 47−66. doi: 10.1007/978-3-319-25808-9_4 |
| [20] |
La P, Bhatnagar S. 2011. Reinforcement learning with function approximation for traffic signal control. |
| [21] |
Mohamad Alizadeh Shabestary S, Abdulhai B. 2022. Adaptive traffic signal control with deep reinforcement learning and high dimensional sensory inputs: case study and comprehensive sensitivity analyses. |
| [22] |
Liang X, Du X, Wang G, Han Z. 2019. A deep reinforcement learning network for traffic light cycle control. |
| [23] |
Ge H, Song Y, Wu C, Ren J, Tan G. 2019. Cooperative deep Q-learning with Q-value transfer for multi-intersection signal control. |
| [24] |
Haddad TA, Hedjazi D, Aouag S. 2022. A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control. |
| [25] |
Chu T, Wang J, Codecà L, Li Z. 2020. Multi-agent deep reinforcement learning for large-scale traffic signal control. |
| [26] |
Bouktif S, Cheniki A, Ouni A. 2021. Traffic signal control using hybrid action space deep reinforcement learning. |
| [27] |
Du Y, ShangGuan W, Rong D, Chai L. 2019. RA-TSC: learning adaptive traffic signal control strategy via deep reinforcement learning. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), October 27–30, 2019. Auckland, New Zealand. USA: IEEE. pp. 3275–80. doi: 10.1109/itsc.2019.8916967 |
| [28] |
Kumar N, Mittal S, Garg V, Kumar N. 2022. Deep reinforcement learning-based traffic light scheduling framework for SDN-enabled smart transportation system. |
| [29] |
Li L, Lv Y, Wang FY. 2016. Traffic signal timing via deep reinforcement learning. |
| [30] |
Kumar N, Rahman SS, Dhakad N. 2021. Fuzzy inference enabled deep reinforcement learning-based traffic light control for intelligent transportation system. |
| [31] |
Ma D, Zhou B, Song X, Dai H. 2022. A deep reinforcement learning approach to traffic signal control with temporal traffic pattern mining. |
| [32] |
Aslani M, Mesgari MS, Wiering M. 2017. Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events. |
| [33] |
Wei H, Zheng G, Gayah V, Li Z. 2019. A survey on traffic signal control methods. |
| [34] |
El-Tantawy S, Abdulhai B. 2010. An agent-based learning towards decentralized and coordinated traffic signal control. 13th International IEEE Conference on Intelligent Transportation Systems, September 19−22, 2010, Funchal, Portugal. USA: IEEE. pp. 665−70. doi: 10.1109/ITSC.2010.5625066 |
| [35] |
Khamis MA, Gomaa W. 2012. Enhanced multiagent multi-objective reinforcement learning for urban traffic light control. 2012 11th International Conference on Machine Learning and Applications, December 12−15, 2012, Boca Raton, FL, USA. USA: IEEE. pp. 586−91. doi: 10.1109/ICMLA.2012.108 |
| [36] |
Mousavi SS, Schukat M, Howley E. 2017. Traffic light control using deep policy-gradient and value-function-based reinforcement learning. |
| [37] |
Zhao J, Yao T, Zhang C, Shafique MA. 2024. Signal control for overflow prevention at intersections using partial connected vehicle data. |
| [38] |
Ma C, Yu C, Zhang C, Yang X. 2023. Signal timing at an isolated intersection under mixed traffic environment with self-organizing connected and automated vehicles. |
| [39] |
Yao T, Zhang C, Zhao J, Gupta A, Mondal S. 2023. Adaptive signal control for overflow prevention at isolated intersections based on fuzzy control. |
| [40] |
Noaeen M, Naik A, Goodman L, Crebo J, Abrar T, et al. 2022. Reinforcement learning in urban network traffic signal control: a systematic literature review. |
| [41] |
Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA. 2017. Deep reinforcement learning: a brief survey. |
| [42] |
Bellman R. 1952. On the theory of dynamic programming. |
| [43] |
Van Hasselt H, Guez A, Silver D. 2016. Deep reinforcement learning with double Q-learning. |
| [44] |
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, et al. 2015. Human-level control through deep reinforcement learning. |
| [45] |
Schaul T, Quan J, Antonoglou I, Silver D. 2015. Prioritized experience replay. |
| [46] |
Wang Z, Schaul T, Hessel M, Hasselt H, Lanctot M, et al. 2016. Dueling network architectures for deep reinforcement learning. International Conference on Machine Learning, New York, USA, 2016. New York, USA: PMLR. pp. 1995–2003. https://proceedings.mlr.press/v48/wangf16.pdf |
| [47] |
Bellemare MG, Dabney W, Munos R. 2017. A distributional perspective on reinforcement learning. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 2017. PMLR. pp. 449–58. https://proceedings.mlr.press/v70/bellemare17a/bellemare17a.pdf |
| [48] |
Fortunato M, Azar MG, Piot B, Menick J, Osband I, et al. 2017. Noisy networks for exploration. |
| [49] |
Hessel M, Modayil J, Van Hasselt H, Schaul T, Ostrovski G, et al. 2018. Rainbow: combining improvements in deep reinforcement learning. |
| [50] |
Gu J, Fang Y, Sheng Z, Wen P. 2020. Double deep Q-network with a dual-agent for traffic signal control. |
| [51] |
Park S, Han E, Park S, Jeong H, Yun I. 2021. Deep Q-network-based traffic signal control models. |
| [52] |
Ducrocq R, Farhi N. 2023. Deep reinforcement Q-learning for intelligent traffic signal control with partial detection. |
| [53] |
Nishi T, Otaki K, Hayakawa K, Yoshimura T. 2018. Traffic signal control based on reinforcement learning with graph convolutional neural nets. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), November 4−7, 2018, Maui, HI, USA. USA: IEEE. pp. 877−83. doi: 10.1109/ITSC.2018.8569301 |
| [54] |
Zang X, Yao H, Zheng G, Xu N, Xu K, et al. 2020. MetaLight: value-based meta-reinforcement learning for traffic signal control. |
| [55] |
Steingrover M, Schouten R, Peelen S, Nijhuis E, Bakker B. 2005. Reinforcement learning of traffic light controllers adapting to traffic congestion. Proceedings of the Seventeenth Belgium-Netherlands Conference on Artificial Intelligence, Brussels, Belgium, October 17−18, 2005. |
| [56] |
Gokulan BP, Srinivasan D. 2010. Distributed geometric fuzzy multiagent urban traffic signal control. |
| [57] |
Salkham A. 2010. Decentralized optimization of fluctuating urban traffic using reinforcement learning. PhD thesis. Trinity College Dublin, UK |
| [58] |
Xu LH, Xia XH, Luo Q. 2013. The study of reinforcement learning for traffic self-adaptive control under multiagent Markov game environment. |
| [59] |
Salkham A, Cunningham R, Garg A, Cahill V. 2008. A collaborative reinforcement learning approach to urban traffic control optimization. 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, December 9−12, 2008, Sydney, NSW, Australia. USA: IEEE. pp. 560−66. doi: 10.1109/WIIAT.2008.88 |
| [60] |
Kuleshov V, Precup D. 2014. Algorithms for multi-armed bandit problems. |
| [61] |
Webster FV. 1958. Traffic signal settings. Road Research Technical Paper, No. 39. London, UK: Department of Scientific and Industrial Research. |
| [62] |
Varaiya P. 2013. Max pressure control of a network of signalized intersections. |