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Traffic Flow Control using Neural Network

Anuja Nagare, Shalini Bhatia Published in Information Systems

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
10.5120/ijais12-450115
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  1. Anuja Nagare and Shalini Bhatia. Article: Traffic Flow Control using Neural Network. International Journal of Applied Information Systems 1(2):50-52, January 2012. BibTeX

    @article{key:article,
    	author = "Anuja Nagare and Shalini Bhatia",
    	title = "Article: Traffic Flow Control using Neural Network",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 1,
    	number = 2,
    	pages = "50-52",
    	month = "January",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

With rapid increase in motorization, urbanization, population growth, and changes in population density, Traffic Congestion problems have increased worldwide. Traffic Congestion causes increase in traveling time, air pollution and increase in fuel usage is also observed. Intelligent Transportation Systems (ITS) are used to avoid these problems and improve efficiency, safety and service. Traffic Flow Forecasting is an important part of ITS [1][2]. Traffic Flow Forecasting (TFF) is for Controlling Traffic and Intelligent Traffic Guidance. TFF is the study of interactions between vehicles, drivers, and infrastructure (which includes highways and traffic control devices), with the aim of understanding and developing an optimal road network with efficient movement of traffic and minimal traffic congestion problems.

Reference

  1. Jimmy Wales, Larry Sanger[Online]. Available : “http://en.wikipedia.org/wiki/Intelligent_transportation_system”, Jan 2012.
  2. Greener, Safer, Smarter, Building Communications for Intelligent Transportation Systems, A Tropos Networks White Paper[Online]. Available: http://www.freeway.gov.tw/UserFiles/File/Traffic/A1%20Brief%20introduction%20to%20Intelligent%20Transportation%20System,%20ITS.pdf, June 2009.
  3. Association mondiale de la Route / World Road Association, AIPCR / PIARC, La Grande Arche Paroi North, France, [Online]. Available: http://road-network-operations.piarc.org/index.php?option=com_content&task=view&id=39&Itemid=215291&lang=en.
  4. Fengying Cui , “Study Of Traffic Flow Prediction Based On Bp Neural Network”, In proc. of College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao, 2010.
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  7. S.Hui, Z.G.Liu, and C.J. Li, “Research on Traffic Flow Forecasting Design Based On BP Neural Network”, Journal of Southwest University of Science and Technology,2008,Vol. 23,No.2,pp.72-75.
  8. A.Zhang and L.Zhang, “RBF neural networks for the prediction of building interference effects”, Computers & Structures, 2004, Vol.82, No.27, pp.2333-2339.
  9. Y.W. Guo, W.D. Li, A.R. Mileham, and G.W. Owen, “Applications of particle swarm optimisation in integrated process planning and scheduling”, Robotics and Computer-Integrated Manufacturing, 2009, Vol.25, No.2, pp.280-288.

Keywords

Back Propagation Neural Network (BPNN), Simulated Annealing Genetic BP Algorithm, RBF Neural Network, Particle Swarm Optimization (PSO) Fish-eye State Routing Protocol (FSR), GRID Fisheye Routing Protocol (GFSR), QoS, NS2, Packet Delivery Ratio, Throughput, Control Overhead, Normalized Overhead, End to End Delay