Possibilistic Clustering Technique-Based Traffic Light Control for Handling Emergency Vehicle
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.
[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
[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
@article{"International Journal of Information, Control and Computer Sciences:51709", author = "F. Titouna and S. Benferhat and K. Aksa and C. Titouna", title = "Possibilistic Clustering Technique-Based Traffic Light Control for Handling Emergency Vehicle", 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.", volume = "6", number = "10", pages = "1185-8", }
