SELF-ORGANIZING URBAN TRAFFIC CONTROL ARCHITECTURE WITH SWARM-SELF ORGANIZING MAP IN JAKARTA: SIGNAL CONTROL SYSTEM AND SIMULATOR

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 3 , ISSUE 3 (September 2010) > List of articles

SELF-ORGANIZING URBAN TRAFFIC CONTROL ARCHITECTURE WITH SWARM-SELF ORGANIZING MAP IN JAKARTA: SIGNAL CONTROL SYSTEM AND SIMULATOR

W. Jatmiko * / A. Azurat / Herry / A. Wibowo / H. Marihot / M. Wicaksana / I. Takagawa / K. Sekiyama / T. Fukuda

Keywords : Traffic Control, Swarm-self Organizing Map, Distributed Traffic Control.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 3, Issue 3, Pages 443-465, DOI: https://doi.org/10.21307/ijssis-2017-402

License : (CC BY-NC-ND 4.0)

Published Online: 13-December-2017

ARTICLE

ABSTRACT

Urban traffic control is the main factor that contributes to traffic jam. Approach in distributed Urban traffic control has been developed in several research, but the coordinating controller factor is basically a quite complicated task to tackle, because between intersection have dependency, so required a method of distributed control system capable for synchronizing between intersections. In this paper we present architecture of decentralized self-organizing traffic control with swarm-self organizing map in real situation even on non-structure intersections like in Jakarta (Indonesia). Based on the proposed architecture we have been implemented Traffic Signal Control System for controlling traffic lights in which the coordination between the intersections is implemented using distributed swarm self-organizing map. Traffic Signal Control System were tested in a simulated real-road scenario of Jakarta. By means of the computer simulation, the application of distributed swarm signal self-organizing control is proved effective in urban traffic.

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[1]N. H.Gartner, S. F.Assmann, F. Lasaga, and D. L. Hom, “A multiband approach to arterial traffic signal optimization,” Transport. Res. B, vol. 25, pp. 55–74, 1991.
[2]M. Patel and N. Ranganathan, “IDUTC: An intelligent decision-making system for urban traffic-control applications,” IEEE Trans. Veh. Technol., vol. 50, pp. 816–829, May 2001.
[3]W. Hong and X.-q. Mu, “A cooperative fuzzy control method for traffic lights,” J. Syst. Simul., vol. 13, no. 5, pp. 551–553, 2001. Chinese.
[4]E. Bingham, “Reinforcement learning in neurofuzzy traffic signal control,”Eur. J. Oper. Res., vol. 131, pp. 232–241, 2002.
[5]Edward J. Davidson and Yukinori Kakazu, “Decentralized Control of Traffic Network,” IEEE Transaction on Automatic Control, vol. AC-28, no. 6, pp. 677-688
[6]Srinivasan, D.; Min Chee Choy; Cheu, R.L. "Neural Networks for Real-Time Traffic Signal Control ". Intelligent Transportation Systems, IEEE Transactions.2006 , Page(s): 261 - 272
[7]K. Sekiyama, et al. 20. “Self-Organizing Control of Urban Traffic Signal Network”. Systems, Man, and Cybernetics. IEEE International Conference on System, Man and Cybernetic 2001, pp. 2481-2486.
[8]Yiyan Wang; Yuexian Zou; Hang Shi; He Zhao. "Video Image Vehicle Detection System for Signaled Traffic Intersection".Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference.Page(s): 222 - 227
[9]Sadayoshi Mikami and Yukinori Kakazu, “Genetic Reinforcement Learning for Cooperative Traffic Signal Control,” in IEEE Conference on Evolutionary Computation, Juni 1994, pp. 223-228.
[10]I. Porche, R. Segupta M. Sampath, Y.-L. Chen, and S. Lafortune, “A Decentralized Scheme for Real-Time Optimization of Traffic Signals,” in IEEE International Conference on Control Application, September. 1996, pp. 582-589
[11]Tadnobu Misaya, Haruhiko Kimura, Sadaki Hirose, and Nobuyasu Osato,“ Multi Agent-Based Traffic Signal Control with Reinforcement Learning,” IEICE Transactions, vol. J83-D-I, no 5, pp. 478-486, Mei 2000.
[12]Ikuku N., Takeshi I., and Kazutoshi S.,” Improvements of the Traffic Signal Control by Complex-Valued Hopfield Networks”, IEEE International Conference World Congress of Evolutionary Computational 2006 (International Joint Conference on Neural Networks 2006), pp. 1186-1191G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of [3]
[13]Yoshiki Kuramoto, Chemical Oscillations, Waves, and Turbulence, Springer- Verlag, 1984.

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