1. bookVolume 21 (2020): Edizione 4 (December 2020)
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1407-6179
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20 Mar 2000
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Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus

Pubblicato online: 26 Nov 2020
Volume & Edizione: Volume 21 (2020) - Edizione 4 (December 2020)
Pagine: 295 - 302
Dettagli della rivista
License
Formato
Rivista
eISSN
1407-6179
Prima pubblicazione
20 Mar 2000
Frequenza di pubblicazione
4 volte all'anno
Lingue
Inglese

1. Bakker, B., Whiteson, S., Kester, L., Groen, F.C.A. (2010) Traffic light control by multiagent reinforcement learning systems. Stud. Comput. Intell. 281, 475–510. https://doi.org/10.1007/978-3-642-11688-9_1810.1007/978-3-642-11688-9_18Search in Google Scholar

2. Buşoniu, L., Babuška, R., De Schutter, B. (2008) A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. https://doi.org/10.1109/TSMCC.2007.91391910.1109/TSMCC.2007.913919Search in Google Scholar

3. Chen, C., Wei, H., Xu, N., Zheng, G., Yang, M., Xiong, Y., Xu, K., Li, Z., 2020. Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control. Aaai 3414–3121.10.1609/aaai.v34i04.5744Search in Google Scholar

4. El-Tantawy, S., Abdulhai, B., Abdelgawad, H. (2013) Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (marlin-atsc): Methodology and large-scale application on downtown toronto. IEEE Trans. Intell. Transp. Syst. 14, 1140–1150. https://doi.org/10.1109/TITS.2013.225528610.1109/TITS.2013.2255286Search in Google Scholar

5. Fellendorf, M., Vortisch, P. (2010) Microscopic traffic flow simulator VISSIM. In: International Series in Operations Research and Management Science. Springer New York LLC, pp. 63–93. https://doi.org/10.1007/978-1-4419-6142-6_210.1007/978-1-4419-6142-6_2Search in Google Scholar

6. Liu, Y., Liu, L., Chen, W.P. (2018) Intelligent traffic light control using distributed multi-agent Q learning. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. pp. 1–8. https://doi.org/10.1109/ITSC.2017.831773010.1109/ITSC.2017.8317730Search in Google Scholar

7. 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. Springer International Publishing, pp. 47–66. https://doi.org/10.1007/978-3-319-25808-9_410.1007/978-3-319-25808-9_4Search in Google Scholar

8. Papageorgiou, M. (2004) Overview of road traffic control strategies. In: IFAC Proceedings Volumes (IFAC-PapersOnline). IFAC Secretariat, pp. 29–40. https://doi.org/10.1016/s1474-6670(17)30657-210.1016/S1474-6670(17)30657-2Search in Google Scholar

9. Penic, M.A., Upchurch, J. (1992) TRANSYT-7F: Enhancement for Fuel Consumption, Pollution Emissions, and User Costs. Transp. Res. Rec. 104–111.Search in Google Scholar

10. Urbanik, T., Tanaka, A., Lozner, B., Lindstrom, E., Lee, K., Quayle, S., Beaird, S., Tsoi, S., Ryus, P., Gettman, D., Sunkari, S., Balke, K., Bullock, D. (2015) Signal Timing Manual – Second Edition, Signal Timing Manual – Second Edition. Transportation Research Board.https://doi.org/10.17226/2209710.17226/22097Search in Google Scholar

11. Wang, Y., Szeto, W.Y., Han, K., Friesz, T.L., 2018. Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications. Transp. Res. Part B Methodol. https://doi.org/10.1016/j.trb.2018.03.01110.1016/j.trb.2018.03.011Search in Google Scholar

12. Zhong, D., Boukerche, A. (2019) Traffic Signal Control Using Deep Reinforcement Learning with Multiple Resources of Rewards. In: Proceedings of the 16th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, ---amp--- Ubiquitous Networks - PE-WASUN ’19. ACM Press, New York, New York, USA, pp. 23–28. https://doi.org/10.1145/3345860.336152210.1145/3345860.3361522Search in Google Scholar

13. Zhu, F., Aziz, H.M.A., Qian, X., Ukkusuri, S. V. (2015) A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework. Transp. Res. Part C Emerg. Technol., 58, 487–501. https://doi.org/10.1016/j.trc.2014.12.00910.1016/j.trc.2014.12.009Search in Google Scholar

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