In this paper, control methodology based on the selection of the type of traffic light and the period of the green phase to accomplish an optimum balance at intersections is proposed. This balance should be flexible to the static behavior of time, and randomness in a traffic situation; the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control over the crash of cars. The proposed method was specifically investigated at the intersection through an appropriate timing of traffic lights by sampling a multi-agent system. It consists of a large number of intersections, each of which is considered as an independent agent that exchanges information with each other, and the stability of each agent is provided separately. The robustness against uncertainties, scalability, and stability of the closed-loop overall system are the main merits of the proposed methodology. The simulation results show that the fuzzy intelligent controller in this multi-factor system which is a Takagi-Sugeno (TS) fuzzy is more useful than scheduling in the fixed-time method and it reduces the lengths of vehicles queuing.
[1] V. Hirankitti and J. Krohkaew, "An agent approach for intelligent traffic-light control," is null, 2007, pp. 496-501.
[2] M. Abdoos, N. Mozayani, and A. L. Bazzan, "Traffic light control in non-stationary environments based on multi agent Q-learning," in Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, 2011, pp. 1580-1585.
[3] F.-Y. Wang, "Agent-based control for networked traffic management systems," IEEE Intelligent Systems, vol. 20, 2005, pp. 92-96.
[4] F.-Y. Wang, "Toward a revolution in transportation operations: AI for complex systems," IEEE Intelligent Systems, vol. 23, 2008.
[5] A. L. Bazzan and F. Klügl, "A review on agent-based technology for traffic and transportation," The Knowledge Engineering Review, vol. 29, 2014, pp. 375-403.
[6] D. Zhao, Y. Dai, and Z. Zhang, "Computational intelligence in urban traffic signal control: A survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, 2012, pp. 485-494.
[7] E. H. Durfee, Coordination of distributed problem solvers vol. 55: Springer Science & Business Media, 2012.
[8] J. Joubert, P. Fourie, and K. Axhausen, "Large-scale agent-based combined traffic simulation of private cars and commercial vehicles," Transportation Research Record: Journal of the Transportation Research Board, 2012, pp. 24-32, 2010.
[9] A. Garcia-Serrano, D. T. Vioque, F. Carbone, and V. Mendez, "FIPA-compliant MAS development for road traffic management with a knowledge-based approach: The TRACK-R agents," in Proc. Challenges Open Agent Syst. Workshop, 2003.
[10] V. R. Tomás and L. A. García, "Agent-based management of non urban road meteorological incidents," in International Central and Eastern European Conference on Multi-Agent Systems, 2005, pp. 213-222.
[11] B. Chen, H. H. Cheng, and J. Palen, "Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems," Transportation Research Part C: Emerging Technologies, vol. 17, 2009, pp. 1-10.
[12] B. Chen, H. H. Cheng, and J. Palen, "Agent-based real-time computing and its applications in traffic detection and management systems," in ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2004, pp. 543-552.
[13] F. L. Bellifemine, G. Caire, and D. Greenwood, Developing multi-agent systems with JADE vol. 7: John Wiley & Sons, 2007.
[14] C. P. Pappis and E. H. Mamdani, "A fuzzy logic controller for a trafc junction," IEEE Transactions on Systems, Man, and Cybernetics, vol. 7, 1977, pp. 707-717.
[15] R. L. Kelsey and K. R. Bisset, "Simulation of traffic flow and control using fuzzy and conventional methods," Fuzzy logic and control: software and hardware applications, 1993, pp. 262-278.
[16] M. B. Trabia, M. S. Kaseko, and M. Ande, "A two-stage fuzzy logic controller for traffic signals," Transportation Research Part C: Emerging Technologies, vol. 7, 1999, pp. 353-367.
[17] L. Zhang, H. Li, and P. D. Prevedouros, "Signal control for oversaturated intersections using fuzzy logic," in Transportation and Development Innovative Best Practices 2008, ed, 2008, pp. 179-184.
[18] J. Li, X. Pan, and X. Wang, "State-space equations and the first-phase algorithm for signal control of single intersections," Tsinghua Science and Technology, vol. 12, 2007, pp. 231-235.
[19] E. Azimi Rad, N. Pariz, and M. B. Naghibi Sistani, "A Novel Fuzzy Model and Control of Single Intersection at Urban Traffic Network," IEEE Systems Journal, vol. 4, 2010, pp. 1-10.
[1] V. Hirankitti and J. Krohkaew, "An agent approach for intelligent traffic-light control," is null, 2007, pp. 496-501.
[2] M. Abdoos, N. Mozayani, and A. L. Bazzan, "Traffic light control in non-stationary environments based on multi agent Q-learning," in Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, 2011, pp. 1580-1585.
[3] F.-Y. Wang, "Agent-based control for networked traffic management systems," IEEE Intelligent Systems, vol. 20, 2005, pp. 92-96.
[4] F.-Y. Wang, "Toward a revolution in transportation operations: AI for complex systems," IEEE Intelligent Systems, vol. 23, 2008.
[5] A. L. Bazzan and F. Klügl, "A review on agent-based technology for traffic and transportation," The Knowledge Engineering Review, vol. 29, 2014, pp. 375-403.
[6] D. Zhao, Y. Dai, and Z. Zhang, "Computational intelligence in urban traffic signal control: A survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, 2012, pp. 485-494.
[7] E. H. Durfee, Coordination of distributed problem solvers vol. 55: Springer Science & Business Media, 2012.
[8] J. Joubert, P. Fourie, and K. Axhausen, "Large-scale agent-based combined traffic simulation of private cars and commercial vehicles," Transportation Research Record: Journal of the Transportation Research Board, 2012, pp. 24-32, 2010.
[9] A. Garcia-Serrano, D. T. Vioque, F. Carbone, and V. Mendez, "FIPA-compliant MAS development for road traffic management with a knowledge-based approach: The TRACK-R agents," in Proc. Challenges Open Agent Syst. Workshop, 2003.
[10] V. R. Tomás and L. A. García, "Agent-based management of non urban road meteorological incidents," in International Central and Eastern European Conference on Multi-Agent Systems, 2005, pp. 213-222.
[11] B. Chen, H. H. Cheng, and J. Palen, "Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems," Transportation Research Part C: Emerging Technologies, vol. 17, 2009, pp. 1-10.
[12] B. Chen, H. H. Cheng, and J. Palen, "Agent-based real-time computing and its applications in traffic detection and management systems," in ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2004, pp. 543-552.
[13] F. L. Bellifemine, G. Caire, and D. Greenwood, Developing multi-agent systems with JADE vol. 7: John Wiley & Sons, 2007.
[14] C. P. Pappis and E. H. Mamdani, "A fuzzy logic controller for a trafc junction," IEEE Transactions on Systems, Man, and Cybernetics, vol. 7, 1977, pp. 707-717.
[15] R. L. Kelsey and K. R. Bisset, "Simulation of traffic flow and control using fuzzy and conventional methods," Fuzzy logic and control: software and hardware applications, 1993, pp. 262-278.
[16] M. B. Trabia, M. S. Kaseko, and M. Ande, "A two-stage fuzzy logic controller for traffic signals," Transportation Research Part C: Emerging Technologies, vol. 7, 1999, pp. 353-367.
[17] L. Zhang, H. Li, and P. D. Prevedouros, "Signal control for oversaturated intersections using fuzzy logic," in Transportation and Development Innovative Best Practices 2008, ed, 2008, pp. 179-184.
[18] J. Li, X. Pan, and X. Wang, "State-space equations and the first-phase algorithm for signal control of single intersections," Tsinghua Science and Technology, vol. 12, 2007, pp. 231-235.
[19] E. Azimi Rad, N. Pariz, and M. B. Naghibi Sistani, "A Novel Fuzzy Model and Control of Single Intersection at Urban Traffic Network," IEEE Systems Journal, vol. 4, 2010, pp. 1-10.
@article{"International Journal of Information, Control and Computer Sciences:80853", author = "Reza Ghasemi and Negin Amiri Hazaveh", title = "Ultimately Bounded Takagi-Sugeno Fuzzy Management in Urban Traffic Stream Mechanism: Multi-Agent Modeling Approach", abstract = "In this paper, control methodology based on the selection of the type of traffic light and the period of the green phase to accomplish an optimum balance at intersections is proposed. This balance should be flexible to the static behavior of time, and randomness in a traffic situation; the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control over the crash of cars. The proposed method was specifically investigated at the intersection through an appropriate timing of traffic lights by sampling a multi-agent system. It consists of a large number of intersections, each of which is considered as an independent agent that exchanges information with each other, and the stability of each agent is provided separately. The robustness against uncertainties, scalability, and stability of the closed-loop overall system are the main merits of the proposed methodology. The simulation results show that the fuzzy intelligent controller in this multi-factor system which is a Takagi-Sugeno (TS) fuzzy is more useful than scheduling in the fixed-time method and it reduces the lengths of vehicles queuing.", keywords = "Fuzzy intelligent controller, traffic-light control, multi-agent systems, state space equations, stability.", volume = "16", number = "6", pages = "239-7", }
