DocumentCode :
3584664
Title :
Elicit the Best Ways through Identify Congestion Places
Author :
Alodat, Mohammad ; Abdullah, Iyas
Author_Institution :
Comput., Electr. & Electron. Eng., Univ. of Craiova, Craiova, Romania
fYear :
2014
Firstpage :
102
Lastpage :
107
Abstract :
In this paper the exploitation of infrastructure and take advantage of communication techniques wired and wireless to help drivers in finding ways does not contain any obstacles and reach to the target with commitment in speed of assessed road. We use Fusion or Polygamy Technology with Fuzzy Logic, Neural Network and Genetic Algorithm for extract Cut-Node (CN). We use Cut-Node (CN) in order to identify obstacles and avoid them such as changes that occur in the lighting, weather conditions, accidents, congestion and maintenance of roads. Cut-Node (CN) useful in understanding the surrounding environment, supporting safe driving, increases the flow and fastest path to arrive at the designated target (end point). Three models have been proposed to extract Cut-Node (CN) as follows: 1) Intuitionistic Fuzzy Set (IFS). 2) The Intuitionistic Fuzzy Set Data Base is representing imprecise data. 3) Intuitionistic Fuzzy Neural Network with Genetic Algorithm (IFNN-GA).
Keywords :
data structures; fuzzy set theory; genetic algorithms; neural nets; road safety; road traffic; traffic engineering computing; cut-node extraction; fusion technology; fuzzy logic; genetic algorithm; imprecise data representation; intuitionistic fuzzy neural network; intuitionistic fuzzy set; polygamy technology; road congestion places identification; safety driving; wired communication technique; wireless communication technique; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Genetic algorithms; Neurons; Roads; Vehicles; Cut Node; Fuzzy logic; Genetic algorithm; Intuitionistic Fuzzy Set; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computer Science Applications and Technologies (ACSAT), 2014 3rd International Conference on
Type :
conf
DOI :
10.1109/ACSAT.2014.25
Filename :
7076877
Link To Document :
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