IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 6    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 6, June 2011 Edition
Bio-inspired Neuro-Fuzzy Based Dynamic Route Selection to Avoid Traffic Congestion
Full Text(PDF, 3000)  PP.  
Author(s)
Sagheer Abbass, M. Saleem Khan, Khalil Ahmed, M.Abdullah, Umer Farooq
KEYWORDS
Bio-inspired, Traffic congestion, Dynamic route selection, artificial neural networks, Bio-inspired Fuzzy based traffic system.
ABSTRACT
this paper presents the bio-inspired neuro- fuzzy based dynamic route selection system to avoid traffic congestion. The proposed intelligent decision making multi-parameter route selection system opts for the best multi-parameter direction between two desired nodes: origin and the destination. This work uses a combination of neuro-fuzzy logic and ant colony system (ACS) algorithm for the prime routing to satisfy all the desired requirements of the user using online traffic data directly delivered by the traffic control center and the traffic flow is predicted by artificial neural networks
References
[1] Talebi, H. S. (2010). Dynamic Fuzzy Logic-Ant Colony System- Based. Applied Computational Intelligence and Soft Computing. Hindawi Publishing Corporation.

[2] Prasana Kumar and Raghavendra,(2011) “On the evaporation mechanism in the ant colony optimization,” Annals computer science series, pp. 51-56

[3] Diogo Alves, Jelmer van Ast, Zhe Cong (2010). Ant Colony Optimization for Traffic Dispersion Routing, 13th International IEEE

[4] Annual Conference on Intelligent Transportation Systems, pp. 683-688

[5] Saliba, C. Farrugia, R.A (2010 quality of service aware Ant colony optimization routing algorithm, 15th IEEE Mediterranean Electrotechnical Conference , 343 - 347

[6] A. Broggi, M. C. (2003). An evolutionary approach to visual sensing for vehicle navigation. IEEE Transactions on Industrial Electronics, vol. 50, no. 1, pp. 18–29.

[7] H. Salehinejad, F. P. (2008). A new route selection system: multiparameter ant algorithm based vehicle navigation approach. Vienna, Austria: in Proceedings of the International Conference on Computational Intelligence for Modeling, Control and Automatio, pp 1089-1094.

[8] Swann, S. A. (1979). Analysis of freeway traffic times-series data by using Box Jenkins techniques. Transportation Research, no. 72, pp. 1–9.

[9] I. Ohe, H. K. (1995). A method for automatic detection of traffic incidents using neural networks. in Proceedings of the Vehicle Navigation and Information Systems Conference in Conjunction with the Pacific Rim TransTech Conference, A Ride into the Future, pp 231-235.

[10] Attia, R. Rizk, R. Mariee, M. . (2009). Wireless and Optical Communications Networks, 2009. WOCN '09. IFIP International Conference on ,pp, 1-5

[11] Meldrum, C. T. (1995). Freeway data prediction using neural networks. in Proceedings of the Vehicle Navigation and Information Systems Conference in Conjunction with the Pacific Rim TransTech Conference, A Ride into the Future, pp. 225–230.

[12] Xiaoming You Xingwai Miao Sheng Liu. (2009) .quantum computing based ant colony optimization algorithm for travelling sales man problem, pp,359-362

Untitled Page