CFP last date
20 June 2024
Reseach Article

Distributed Learning Automata based Route Planning

Published on March 2015 by K.b. Priya Iyer, V. Shanthi, Alamelu L
International Conference on Communication, Computing and Information Technology
Foundation of Computer Science USA
ICCCMIT2014 - Number 2
March 2015
Authors: K.b. Priya Iyer, V. Shanthi, Alamelu L
5bcdee4c-3e7d-4392-900f-72ac7bb16043

K.b. Priya Iyer, V. Shanthi, Alamelu L . Distributed Learning Automata based Route Planning. International Conference on Communication, Computing and Information Technology. ICCCMIT2014, 2 (March 2015), 29-32.

@article{
author = { K.b. Priya Iyer, V. Shanthi, Alamelu L },
title = { Distributed Learning Automata based Route Planning },
journal = { International Conference on Communication, Computing and Information Technology },
issue_date = { March 2015 },
volume = { ICCCMIT2014 },
number = { 2 },
month = { March },
year = { 2015 },
issn = 0975-8887,
pages = { 29-32 },
numpages = 4,
url = { /proceedings/icccmit2014/number2/19776-7022/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Communication, Computing and Information Technology
%A K.b. Priya Iyer
%A V. Shanthi
%A Alamelu L
%T Distributed Learning Automata based Route Planning
%J International Conference on Communication, Computing and Information Technology
%@ 0975-8887
%V ICCCMIT2014
%N 2
%P 29-32
%D 2015
%I International Journal of Computer Applications
Abstract

Distributed Learning Automata is automata based modelling approach for solving stochastic shortest path problems. The DLA can be applied to road networks to find shortest path that provides a spatial approach to bottom-up modelling of complex geographic systems that are comprised of infrastructure and human objects. Route finding is a popular Geographical Information System (GIS) application under Intelligent Transportation Systems (ITS). TheITS reduce traffic congestion and improve road network performance. They provide real time traffic information and route recommendation to road users, to increase their ability to choose the best alternative path. In recent years, DLA models for urban growth simulation are gaining popularity because of their ability to incorporate the spatial and temporal dimensions of the processes. The usage of DLA in route finding has not been explored to its fullest. In this work, a routing finding algorithm is proposed on road networks using DLA. The Distributed Learning Automata based Route Planning (DLARP) algorithm based on two dimensional automata is proposed. Transition rule of automata are proposed in such a way that at each step time the user cell is exchanged with best goal directing cell. DLARP algorithm achieves a proper solution to route finding in spatial road networks.

References
  1. Adel Akbarimajd, Akbar HasanZadeh. A Novel Cellular Automata Based Real Time Path Planning Method for Mobile Robots. International Journal of Engineering Research and Applications (IJERA) Vol. 1, Issue 4, pp. 1262-1267.
  2. C. Behring, M. Bracho, M. Castro, J. A. Moreno, and Emergente, 2000. An algorithm for robot path planning with cellular automata. in Proceedings of the Fourth Int. Conference on Cellular Automata for Research and Industry.
  3. Benenson, I. , and P. M. Torrens. 2004. Geosimulation: object-based modeling of urban phenomena. Computers, Environment and Urban Systems.
  4. Benenson, I. , and P. M. Torrens 2004. Special Issue: Geosimulation: object-based modeling of urban phenomena. Computers, Environment and Urban Systems.
  5. S. C. Benjamin, N. F. Johnson, P. M. Hui. 1996. Journal of Phys. A 29, 3119.
  6. E. G. Campari1 and G. Levi. 2000. A cellular automata model for highway traffic. THE EUROPEAN PHYSICAL JOURNAL. B 17:159-166.
  7. Creamer, M. and J. Ludwig. 1986. A fast simulation model for traffic flow on the basis of Boolean operations. Mathematical and Computers in Simulation, Vol 28, pp: 297-303.
  8. IG. Georgoudas, GC. Sirakoulis, I. Andreadis, 2007. Modeling earthquake activity features using cellular automata, Journal of Mathematical and Computer Modelling, Vol. 46, Issues 1-2, pp 124-137.
  9. Gotts, N. , 2000, Emergent Phenomena in Large Sparse Random Arrays of Conway's 'Game of Life', International Journal of System Science, Vol. 31, No. 7, pp 873-894.
  10. J. Han, Y. Hayashi, X. Cao, H. Imura, 2009. Application of an integrated system dynamics and cellular automata model for urban growth assessment: A case study of Shanghai, China, Journal of Landscape and Urban Planning, Vol. 91, Issue 3, pp 133-141.
  11. F. M. Marchese. 2002. A directional diffusion algorithm on cellular automata for robot path-planning, Future Generation Computer Systems, Vol. 18, Issue 7, pp 983 – 994.
  12. S Murata, H Kurokawa, 2007. Self-reconfigurable robots, IEEE Robotics and Automation Magazine, Vol. 14, Issue 1, March 2007.
  13. K. Nagel and M. Shreckenberg. A cellular automaton model for freeway traffic. J. Phisique I. 1992, 2(12):2221-2229.
  14. Paul L. Rosin. 2006. Training cellular automata for image processing, IEEE Transaction on Image Processing, Vol. 15 Issue: 7, July 2006.
  15. Y. Tao and D. Papadias. 2002. Time-parameterized queries in spatio-temporal databases. In SIGMOD, 2002.
  16. Tavakoli, Y. , Javadi, H. , and Adabi, S. , 2008. A cellular automata based algorithm for path planning in multi-agent systems with a common goal, International journal of computer science and network security, Vol. 8, No. 7, 2008.
  17. Terrazas, G. , Slepmann, P. , Kendall, G. , and Krasnogor, N. , 2007. An evolutionary methodology for automated design of cellular automaton-based complex systems, Journal of cellular automata, vol. pp 77-102.
  18. Von Neumann, J. The general and logical theory of automata, in J. von Neumann, CollectedWorks, edited by A. H. Taub, 5,1963: 288.
  19. Von Neumann, 1966. J. Theory of Self-Reproducing Automata, edited by A. W. Burks (University ofIllinois, Urbana).
  20. J. Weimar. Cellular automata for reactive systems. PhD thesis, UniversiteLibre de Bruxelles, Jan 1995.
  21. X. Xiao, S. Shao, Y. Ding, Z. Huang and K. -C. Chou, 2006. Using cellular automata images and pseudo amino acid composition to predict protein sub cellular location, Journal of Amino Acids, Vol. 30, No 1, pages 49-54.
  22. Yasser F. Hassan. 2011. Journal of Emerging Trends in Computing and Information Sciences, VOL. 2, NO. 9, September 2011.
  23. Zaiben Chen, Heng Tao Shen, Xiaofang Zhou and Jeffrey Xu Yu. 2009. Monitoring Path Nearest Neighbour in Road Networks. SIGMOD 2009.
  24. Zhou C. , Sun Z. and Xie Y. , 2001. The Study on Geo-Cellular Automata. Science press, 34-38, 59-67.
Index Terms

Computer Science
Information Sciences

Keywords

Distributed Learning Automata Cellular Automata Multi-agent Systems Route Planner Intelligent Transportation Systems Spatial Databases Gis Nearest Neighbor Gps Location Based Services Path Planning.