Publication Cover
Journal of Intelligent Transportation Systems
Technology, Planning, and Operations
Volume 27, 2023 - Issue 2
350
Views
1
CrossRef citations to date
0
Altmetric
Research Article

A decentralized intersection management system through collaborative negotiation between smart signals

ORCID Icon, ORCID Icon & ORCID Icon
Pages 272-294 | Received 20 Jun 2019, Accepted 06 Dec 2021, Published online: 26 Dec 2021

References

  • Acosta, A. F., Espinosa, J. E., & Espinosa, J. (2015). Traci4matlab: enabling the integration of the sumo road traffic simulator and matlab® through a software re-engineering process. Modeling mobility with open data (pp. 155–170). Springer.
  • Arel, I., Liu, C., Urbanik, T., & Kohls, A. G. (2010). Reinforcement learning-based multi-agent system for network traffic signal control. IET Intelligent Transport Systems, 4(2), 128–135. https://doi.org/10.1049/iet-its.2009.0070
  • Aziz, H. A., Zhu, F., & Ukkusuri, S. V. (2018). Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility. Journal of Intelligent Transportation Systems, 22(1), 40–52. https://doi.org/10.1080/15472450.2017.1387546
  • Balaji, P., German, X., & Srinivasan, D. (2010). Urban traffic signal control using reinforcement learning agents. IET Intelligent Transport Systems, 4(3), 177–188. https://doi.org/10.1049/iet-its.2009.0096
  • Bazzan, A. L. (2009). Opportunities for multiagent systems and multiagent reinforcement learning in traffic control. Autonomous Agents and Multi-Agent Systems, 18(3), 342–375. https://doi.org/10.1007/s10458-008-9062-9
  • Blogg, M., Semler, C., Hingorani, M., & Troutbeck, R. (2010). Travel time and origin-destination data collection using bluetooth mac address readers. Australasian Transport Research Forum, 36. https://www.australasiantransportresearchforum.org.au/sites/default/files/2010_Blogg_Semler_Hingorani_Troutbeck.pdf
  • Codecá, L., Frank, R., Faye, S., & Engel, T. (2017). Luxembourg sumo traffic (lust) scenario: Traffic demand evaluation. IEEE Intelligent Transportation Systems Magazine, 9(2), 52–63. https://doi.org/10.1109/MITS.2017.2666585
  • Cook, F. S. (2004). Vehicle traffic monitoring using cellular telephone location and velocity data. U.S. Patent and Trademark Office. (US Patent 6,810,321).
  • 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 Transactions on Intelligent Transportation Systems, 14(3), 1140–1150. https://doi.org/10.1109/TITS.2013.2255286
  • Genders, W., & Razavi, S. (2016). Using a deep reinforcement learning agent for traffic signal control, unpublished paper [Online]. Available: https://arxiv.org/abs/1611.01142.
  • Genders, W., & Razavi, S. (2018). Evaluating reinforcement learning state representations for adaptive traffic signal control. Procedia Computer Science, 130, 26–33. https://doi.org/10.1016/j.procs.2018.04.008
  • Genders, W., & Razavi, S. (2019). Asynchronous n-step q-learning adaptive traffic signal control. Journal of Intelligent Transportation Systems, 23(4), 319–331. https://doi.org/10.1080/15472450.2018.1491003
  • Gokulan, B. P., & Srinivasan, D. (2010). Distributed geometric fuzzy multi agent urban traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 11(3), 714–727. https://doi.org/10.1109/TITS.2010.2050688
  • Haddouch, S., Hachimi, H., & Hmina, N. (2018). Modeling the flow of road traffic with the sumo simulator. 2018 4th International Conference on Optimization and Applications (ICOA) (pp. 1–5). IEEE. https://doi.org/10.1109/ICOA.2018.8370580
  • Hunt, P., Robertson, D., Bretherton, R., & Winton, R. (1981). SCOOT-a traffic responsive method of coordinating signals (Tech. Rep.).
  • Hwang, K. S., Jiang, W. C., & Chen, Y. J. (2014). Model learning and knowledge sharing for a multiagent system with Dyna-q learning. IEEE Transactions on Cybernetics, 45(5), 978–990.
  • Jie, L., Van Zuylen, H., Chunhua, L., & Shoufeng, L. (2011). Monitoring travel times in an urban network using video, GPS and bluetooth. Procedia - Social and Behavioral Sciences, 20, 630–637. https://doi.org/10.1016/j.sbspro.2011.08.070
  • Jin, J., & Ma, X. (2015). Adaptive group-based signal control by reinforcement learning. Transportation Research Procedia, 10, 207–216. https://doi.org/10.1016/j.trpro.2015.09.070
  • Jin, J., & Ma, X. (2017). A group-based traffic signal control with adaptive learning ability. Engineering Applications of Artificial Intelligence, 65, 282–293. https://doi.org/10.1016/j.engappai.2017.07.022
  • Khamis, M. A., & Gomaa, W. (2014). Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework. Engineering Applications of Artificial Intelligence, 29, 134–151. https://doi.org/10.1016/j.engappai.2014.01.007
  • Khumara, M. A. D., Fauziyyah, L., & Kristalina, P. (2018). Estimation of urban traffic state using simulation of urban mobility (sumo) to optimize intelligent transport system in smart city. 2018 International Electronics Symposium on Engineering Technology and Applications (IES-ETA) (pp. 163–169). IEEE. https://doi.org/10.1109/ELECSYM.2018.8615508
  • Kok, J. R., & Vlassis, N. (2006). Collaborative multiagent reinforcement learning by payoff propagation. Journal of Machine Learning Research, 7, 1789–1828. http://jmlr.org/papers/v7/kok06a.html
  • Krizan, J., Ertl, L., Bradac, M., Jasansky, M., & Andreev, A. (2014). Automatic code generation from Matlab/Simulink for critical applications. 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1–6). IEEE. https://doi.org/10.1109/CCECE.2014.6901058
  • Li, L., Lv, Y., & Wang, F. Y. (2016). Traffic signal timing via deep reinforcement learning. IEEE/CAA Journal of Automatica Sinica, 3(3), 247–254.
  • Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y. P., Hilbrich, R., Lücken L., Rummel J., Wagner P., Wiessner E. (2018). Microscopic traffic simulation using sumo. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 2575–2582). IEEE. https://doi.org/10.1109/ITSC.2018.8569938
  • Mannion, P., Duggan, J., & Howley, E. (2016). An experimental review of reinforcement learning algorithms for adaptive traffic signal control. Autonomic road transport support systems (pp. 47–66). Springer.
  • Medina, J. C., & Benekohal, R. F. (2012). Traffic signal control using reinforcement learning and the max-plus algorithm as a coordinating strategy. 2012 15th International IEEE Conference on Intelligent Transportation Systems (pp. 596–601). IEEE.
  • Panait, L., & Luke, S. (2005). Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3), 387–434. https://doi.org/10.1007/s10458-005-2631-2
  • Płaczek, B. (2018). A hierarchical cellular automaton model of distributed traffic signal control. arXiv Preprint arXiv:1809.10892.
  • Pol, E. V. D. (2016). Deep reinforcement learning for coordination in traffic light control [master’s thesis]. University of Amsterdam.
  • Prabuchandran, K., An, H. K., & Bhatnagar, S. (2015). Decentralized learning for traffic signal control. 2015 7th International Conference on Communication Systems and Networks (COMSNETS) (pp. 1–6). IEEE. https://doi.org/10.1109/COMSNETS.2015.7098712
  • Rajkumar, S., Prethi, V., & Priyadharshini, B. (2017). Traffic signal control using web-camera and traffic flow estimation [Paper presentation]. Proceedings of the International Conference on Intelligent Computing Systems (ICICS 2017–Dec 15–16th 2017) Organized by Sona College of Technology, Salem, Tamilnadu, India.
  • Saradha, B. J., Vijayshri, G., & Subha, T. (2017). Intelligent traffic signal control system for ambulance using RFID and cloud. 2017 2nd International Conference on Computing and Communications Technologies (ICCCT) (pp. 90–96). IEEE. https://doi.org/10.1109/ICCCT2.2017.7972255
  • Schrank, D., Eisele, B., & Lomax, T. (2012). Tti’s 2012 urban mobility report (p. 4). Texas A&M Transportation Institute.
  • Sims, A. G., & Dobinson, K. W. (1980). The sydney coordinated adaptive traffic (scat) system philosophy and benefits. IEEE Transactions on Vehicular Technology, 29(2), 130–137. https://doi.org/10.1109/T-VT.1980.23833
  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
  • Sutton, R. S., Barto, A. G., et al. (1998). Introduction to reinforcement learning (Vol. 135). MIT Press Cambridge.
  • Wang, F., Tang, K., Li, K., Liu, Z., & Zhu, L. (2019). A group-based signal timing optimization model considering safety for signalized intersections with mixed traffic flows. Journal of Advanced Transportation, 2019, 1–13. https://doi.org/10.1155/2019/2747569
  • Watkins, C. J. C. H. (1989). Learning from delayed rewards [Ph. D thesis]. King’s College, Cambridge.
  • Webster, F. V. (1958). Traffic signal settings (Tech. Rep.).
  • Wongpiromsarn, T., Uthaicharoenpong, T., Wang, Y., Frazzoli, E., & Wang, D. (2012). Distributed traffic signal control for maximum network throughput. 2012 15th International IEEE Conference on Intelligent Transportation Systems (pp. 588–595). IEEE.
  • Xing, S. Y., Lian, G. L., Yan, D. Y., & Cao, J. Y. (2018). Traffic signal light optimization control based on fuzzy control and CCD camera technology. DEStech Transactions on Computer Science and Engineering, https://doi.org/10.12783/dtcse/cmsms2018/25247
  • Yau, K. L A., Qadir, J., Khoo, H. L., Ling, M. H., & Komisarczuk, P. (2017). A survey on reinforcement learning models and algorithms for traffic signal control. ACM Computing Surveys, 50(3), 1–38. https://doi.org/10.1145/3068287
  • Yousef, K. M., Al-Karaki, M. N., & Shatnawi, A. M. (2010). Intelligent traffic light flow control system using wireless sensors networks. Journal of Information Science and Engineering, 26(3), 753–768.
  • Zhao, D., Dai, Y., & Zhang, Z. (2011). Computational intelligence in urban traffic signal control: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 485–494.
  • Zhu, F., Aziz, H. A., Qian, X., & Ukkusuri, S. V. (2015). A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework. Transportation Research Part C: Emerging Technologies, 58, 487–501. https://doi.org/10.1016/j.trc.2014.12.009

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.