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Journal of Engineering and Applied Sciences
Year: 2017 | Volume: 12 | Issue: 9 | Page No.: 2437-2445
DOI: 10.3923/jeasci.2017.2437.2445  
An Efficient Architectural Model for Building Cognitive Expert System Related to Traffic Management in Smart Cities
D.B.K. Kamesh , D.S.K. Sumadhuri , M.S.V. Sahithi and J.K.R. Sastry
 
Abstract: One of the major goals of developing countries is to build smart cities to avoid different kinds of congestions, accidents and many kinds of inordinate delays. The most important consideration is intelligent traffic management system. An intelligent traffic management system can be conceived through many of individual sub-systems which include Bio-sensing system, imaging system, messaging system, cognitive system and visualization system, remote sensing and communication system. Each of the sub-system while is expected to research independently it should also be in existence in unison along with other sub-systems. To implement automated traffic control system there is a need of cognitive subset which is the decisive-core of the integrated system. It essentially researches like a virtual human operator. An embedded remote-control takes in various traffic conditions such as undetected accidents, VIP movement and abnormal environmental conditions as inputs from the police force to the cognitive control system to control the traffic flows at signal post systems. Designing a cognitive subsystem with high precision, to take real-time decisions with varying multiple inputs is a complex task. It should take inputs from all the other subsystems and the man-operator, process the gathered data and then issue control signals accordingly. This study emphasises on the design and application of the cognitive expert system in a simple yet efficient manner to suite the smart city environment.
 
How to cite this article:
D.B.K. Kamesh, D.S.K. Sumadhuri, M.S.V. Sahithi and J.K.R. Sastry, 2017. An Efficient Architectural Model for Building Cognitive Expert System Related to Traffic Management in Smart Cities. Journal of Engineering and Applied Sciences, 12: 2437-2445.
DOI: 10.3923/jeasci.2017.2437.2445
URL: http://medwelljournals.com/abstract/?doi=jeasci.2017.2437.2445