Research of Intelligent Control of Traffic Signal

Abstract:

At present, in the field of intelligent control of traffic signal, most of scholars at home and abroad use fuzzy control and intelligent algorithm, such as genetic algorithm, ant colony optimization, particle swarm optimization, multi-agent, artificial neural networks, fuzzy method etc. This paper summarizes and analyzes these algorithms, points out the problems and shortcomings in the present research, puts forward the direction and trend in the future research. These works have certain directive significance to the research and development of intelligent control of traffic signal.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 107-111
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.107
• Online since: September 2014
• Authors: Xiang Li*, Jin Song Du, Jing Tao Hu, Xin Bi
• Keywords: Fuzzy Control, Intelligent Algorithm, Intelligent Control, Traffic Signal

References

[1] Chen X, Shi Z, Dynamic Optimization Method of Traffic Signal Based on Genetic Algorithm, Journal of system simulation, Vol. 16 (2004) p.1157.

[2] Li R, Lu H, Research of Coordination Fuzzy Control Model of Traffic Trunk Line Based on Genetic Algorithm, Proceedings of the 6th World Congress on Intelligent Control and Automation, Vol. 6 (2006).

DOI: 10.1109/wcica.2006.1713658

[3] Zhang D, Modifications and Applications of Genetic Algorithm and Particle Swarm Optimization, Huazhong University Of Science And Technology, (2007).

[4] Wen Y, Wu T, Real-time Rolling Optimization of Urban Traffic Control Based on Ant Colony Algorithm, Control and Decision, Vol. 19 (2004) p.1063.

[5] He J, Traffic Signal Timing Optimization Based on Ant Colony Algorithm, Shanxi University Of Science And Technology, (2012).

[6] Qu G, Chen S, Application of Particle Swarm Optimization in Traffic Signal Timing, Journal of Guangxi Normal University, Vol. 24 (2006).

[7] Fu S, Huang H, Discrete Control Model and Optimization Algorithm of Traffic Signal, Ordnance Automation, Vol. 6 (2006) p.70.

[8] Xing G, The Study on Urban Traffic Signal Intelligent Control Method at Single Intersection, Zhengzhou University, (2009).

[9] Burmeister B, Doormann J and Matylis G, Agent-oriented traffic simulation, Transactions of the Society for Computer Simulation, Vol. 14 (1997) p.86.

[10] Goldman C V, Rosenschein J S, Evolutionary patterns of agent organizations, IEEE Trans Systems, Man and Cybernetics, Part A. (2002).

DOI: 10.1109/3468.995535

[11] Gao H, Yu G and Li Z, Urban Traffic Signal Control Based on Agent, Control and Decision, Vol. 19 (2004) p.740.

[12] Liu H, Wei W and Peng C, Urban Traffic Control and Induction Coordination Research Based on Multi-Agent, Highways & Automotive Applications, Vol. 5 (2007) p.44.

[13] Li X, Liu Z and Wu J, An Approach of Intersection Traffic Signal Control, Information and Control, Vol. 30 (2001) p.77.

[14] Zhang K, Wei W, Li C, Liu H and Zhang J, Control Method of Intersection Signal Based on Fuzzy Neutral Network, Highways & Automotive Applications, Vol. 5 (2007) p.42.

[15] Zhao X, Research on the Application of Genetic Neural Network in Traffic Control, Jilin University, (2009).

[16] Chen S, Chen H and Xu J, Two Level Fuzzy Control and Simulation for Urban Traffic Single Intersection, Journal of system simulation, Vol. 4 (1998) p.140.

[17] Zhao C, Hu F and Shi P, Study on Fuzzy Signal Control, Computer Engineering, Vol. 6 (2003) p.52.

[18] Hong W, Mou X, Wang Y, Qin Z and Zhang L, A Cooperative Fuzzy Control Method for Traffic Lights, Journal of system simulation, Vol. 9 (2001), p.553.

[19] Lv Z, Hao P, Bi-System Fuzzy Control of Single Intersection, Information and Control, Vol. 30 (2001), p.560.