Possibilistic Clustering Technique-Based Traffic Light Control for Handling Emergency Vehicle
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
Possibilistic Clustering Technique-Based Traffic Light Control for Handling Emergency Vehicle

Authors: F. Titouna, S. Benferhat, K. Aksa, C. Titouna

Abstract:

A traffic light gives security from traffic congestion,reducing the traffic jam, and organizing the traffic flow. Furthermore,increasing congestion level in public road networks is a growingproblem in many countries. Using Intelligent Transportation Systemsto provide emergency vehicles a green light at intersections canreduce driver confusion, reduce conflicts, and improve emergencyresponse times. Nowadays, the technology of wireless sensornetworks can solve many problems and can offer a good managementof the crossroad. In this paper, we develop a new approach based onthe technique of clustering and the graphical possibilistic fusionmodeling. So, the proposed model is elaborated in three phases. Thefirst one consists to decompose the environment into clusters,following by the fusion intra and inter clusters processes. Finally, wewill show some experimental results by simulation that proves theefficiency of our proposed approach.KeywordsTraffic light, Wireless sensor network, Controller,Possibilistic network/Bayesain network.

Keywords: Traffic light, Wireless sensor network, Controller, Possibilistic network/Bayesain network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057497

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1769

References:


[1] Levinson, D. The value of advanced traveler information systems forroute choice. Transportation Research Part C: Emerging Technologies, 11-1:7587. (2003).
[2] G. Leduc, Road Traffic Data: Collection Methods and Applications,Working Papers on Energy, Transport and Climate Change N.1, JRC47967 2008.
[3] Guidelines for Trac Signals, Steering Committee Traffic Control andTraffic Safety, Edition 1992.
[4] D. De Souza Dutra, Traffic light prediction, Internship Report, July2009.
[5] Traffic Detector Handbook, 2006.
[6] http://en.wikipedia.org/wiki/Traffic_signal_preemption
[7] Ben-Gal I., Bayesian Networks, in Ruggeri F., Faltin F. & Kenett R.,Encyclopedia of Statistics in Quality & Reliability, Wiley & Sons(2007).
[8] http://ptolemy.eecs.brekeley.edu.
[9] http://ec.europa.eu/transport/its/index_en.htm.
[10] Thorpe, T. (1997). Vehicle traffic light control using sarsa. Mastersthesis, Department of Computer Science, Colorado State University.
[11] Findler, N. and Stapp, J. (1992). A distributed approach to optimizedcontrol of street traffic signals. Journal of Transportation Engineering,118-1:99110.
[12] Tan, K. K., Khalid, M., and Yusof, R. (1995). Intelligent traffic lightscontrol by fuzzy logic. Malaysian Journal of Computer Science, 9-2.
[13] Taale, H., Back, T., Preu, M., Eiben, A. E., de Graaf, J. M., andSchippers, C. A. (1998). Optimizing traffic light controllers by means ofevolutionary algorithms. In EUFIT98.
[14] Iván Corredor Pérez,Ana-B García,José-F Martínez,Pedro López Bustos. Wireless Sensor Network-based system for measuring and monitoring road traffic(2008).
[15] Kazi Chandrima Rahman, A Survey on Sensor Network, JCIT 2010, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (Online), Volume 01, Issue 01, Manuscript Code: 100715, 2010.
[16] Jamal N. Al-Karaki, Ahmed E. Kamal, Routing Techniques in Wireless Sensor Networks: A Survey, Dept. of Electrical and Computer Engineering Iowa State University, Ames, Iowa 50011.
[17] G. Acs and L. Buttyabv. “A taxonomy of routing protocols for wireless sensor networks,” BUTE Telecommunication department, Jan. 2007.
[18] Jensen, F. V., and Nielsen, T. D. 2007. Bayesian Networks and, Decision Graphs (Information Science and Statistics). Springer.
[19] D Dubois Possibility theory and statistical reasoning Computational Statistics and Data Analysis, 51(1): 47-69, 2006
[20] Benferhat,S and Titouna.F. 2011. On the Fusion of Possibilistic Networks. In IEA/AIE, pp 49-58.
[21] Darwiche, A.: Modeling and Reasoning with Bayesian Networks. Cambridge University Press, Cambridge (2009).
[22] D Dubois and H Prade Possibility theory in information fusion. Data Fusion and Perception, Riccia, Lenz, and Kruse (eds.), CISM Courses and Lectures Vol 431:53-76, Springer-Verlag, 2001.
[23] J. Lin, A.O. Mendelson (1998). Merging databases under constraints. Int. Journ. of Cooperative Information Systems, 7(1), pp. 55-76.
[24] http://www.its.dot.gov
[25] W. R. Heinzelman and P. Chandrakasan. An application-specific protocol architectures for wireless networks. IEEE Transactions on Wireless Communications, 1:660–670, 2002.
[26] L Qing, Q. Zhu, M. Wang. Design of a distributed energy-e