Help the Genetic Algorithm to Minimize the Urban Traffic on Intersections

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Author(s):
Dadmehr Rahbari
Published Date:
September 05, 2014
Issue:
Volume 4, Issue 4
Page(s):
1 - 9
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3374
Downloads:
109

Keywords:
urban traffic, petri net, genetic algorithm
Citation:
Dadmehr Rahbari, "Help the Genetic Algorithm to Minimize the Urban Traffic on Intersections". International Journal of Research in Computer Science, 4 (4): pp. 1-9, September 2014. Other Formats

Abstract

Control of traffic lights at the intersections of the main issues is the optimal traffic. Intersections to regulate traffic flow of vehicles and eliminate conflicting traffic flows are used. Modeling and simulation of traffic are widely used in industry. In fact, the modeling and simulation of an industrial system is studied before creating economically and when it is affordable. The aim of this article is a smart way to control traffic. The first stage of the project with the objective of collecting statistical data (cycle time of each of the intersection of the lights of vehicles is waiting for a red light) steps where the data collection found optimal amounts next it is. Introduced by genetic algorithm optimization of parameters is performed. GA begin with coding step as a binary variable (the range specified by the initial data set is obtained) will start with an initial population and then a new generation of genetic operators mutation and crossover and will Finally, the members of the optimal fitness values are selected as the solution set. The optimal output of Petri nets CPN TOOLS modeling and software have been implemented. The results indicate that the performance improvement project in intersections traffic control systems. It is known that other data collected and enforced intersections of evolutionary methods such as genetic algorithms to reduce the waiting time for traffic lights behind the red lights and to determine the appropriate cycle.

  1. H. Zhonghe, C. Yangzhou, S. Jianjun, W. Xu, and G. Jizhen, “Consensus based Approach to the Signal Control of Urban Traffic Networks,” Procedia - Soc. Behav. Sci., vol. 96, no. Cictp, pp. 2511–2522, Nov. 2013.
  2. M. Madireddy, B. De Coensel, A. Can, B. Degraeuwe, B. Beusen, I. De Vlieger, and D. Botteldooren, “Assessment of the impact of speed limit reduction and traffic signal coordination on vehicle emissions using an integrated approach,” Transp. Res. Part D Transp. Environ., vol. 16, no. 7, pp. 504–508, Oct. 2011.
  3. D. McKenney and T. White, “Distributed and adaptive traffic signal control within a realistic traffic simulation,” Eng. Appl. Artif. Intell., vol. 26, no. 1, pp. 574–583, Jan. 2013.
  4. M. Dotoli, M. P. Fanti, and C. Meloni, “A signal timing plan formulation for urban traffic control,” Control Eng. Pract., vol. 14, no. 11, pp. 1297–1311, Nov. 2006.
  5. S. Barzegar, M. Davoudpour, M. R. Meybodi, a. Sadeghian, and M. Tirandazian, “Formalized learning automata with adaptive fuzzy coloured Petri net; an application specific to managing traffic signals,” Sci. Iran., vol. 18, no. 3, pp. 554–565, Jun. 2011.
  6. F. Basile, P. Chiacchio, and D. Teta, “A hybrid model for real time simulation of urban traffic,” Control Eng. Pract., vol. 20, no. 2, pp. 123–137, Feb. 2012.
  7. F. Corman, A. D’Ariano, I. a. Hansen, and D. Pacciarelli, “Optimal multi-class rescheduling of railway traffic,” J. Rail Transp. Plan. Manag., vol. 1, no. 1, pp. 14–24, Nov. 2011.
  8. H. Mu, J. Yu, and L. Liu, “Evacuation Routes Optimization with Effects of Traffic Light at Intersections,” J. Transp. Syst. Eng. Inf. Technol., vol. 11, no. 3, pp. 76–82, Jun. 2011.
  9. H. Dezani, R. D. S. Bassi, N. Marranghello, L. Gomes, F. Damiani, and I. Nunes da Silva, “Optimizing urban traffic flow using Genetic Algorithm with Petri net analysis as fitness function,” Neurocomputing, vol. 124, pp. 162–167, Jan. 2014.
  10. A. Di Febbraro and N. Sacco, “On Evaluating Traffic Lights Performance Sensitivity via Hybrid Systems Models,” Procedia - Soc. Behav. Sci., vol. 111, pp. 272–281, Feb. 2014.
  11. F. C. Fang, W. L. Xu, K. C. Lin, F. Alam, and J. Potgieter, “Matsuoka Neuronal Oscillator for Traffic Signal Control Using Agent-based Simulation,” Procedia Comput. Sci., vol. 19, no. Ant, pp. 389–395, Jan. 2013.
  12. F. Kaakai, S. Hayat, and A. El Moudni, “A hybrid Petri nets-based simulation model for evaluating the design of railway transit stations,” Simul. Model. Pract. Theory, vol. 15, no. 8, pp. 935–969, Sep. 2007.
  13. A. L. C. Bazzan, D. de Oliveira, and B. C. da Silva, “Learning in groups of traffic signals,” Eng. Appl. Artif. Intell., vol. 23, no. 4, pp. 560–568, Jun. 2010.
  14. M. P. Fanti, a. Giua, and C. Seatzu, “Monitor design for colored Petri nets: An application to deadlock prevention in railway networks,” Control Eng. Pract., vol. 14, no. 10, pp. 1231–1247, Oct. 2006.
  15. T. Becher, “A New Procedure to Determine a User-oriented Level of Service of Traffic Light Controlled Crossroads,” Procedia - Soc. Behav. Sci., vol. 16, pp. 515–525, Jan. 2011.
  16. Z.-J. Ding, X.-Y. Sun, and B.-H. Wang, “Violating traffic light behavior in the Biham-Middleton-Levine traffic flow model,” Procedia Eng., vol. 31, no. 2011, pp. 1072–1076, Jan. 2012.
  17. K. Dahal, K. Almejalli, and M. A. Hossain, “Decision support for coordinated road traffic control actions,” Decis. Support Syst., vol. 54, no. 2, pp. 962–975, Jan. 2013.
  18. D. Formanowicz, A. Sackmann, P. Formanowicz, and J. Błazewicz, “Petri net based model of the body iron homeostasis.,” J. Biomed. Inform., vol. 40, no. 5, pp. 476–85, Oct. 2007.
  19. J. Blazewicz, D. Formanowicz, P. Formanowicz, A. Sackmann, and M. Sajkowski, “Modeling the process of human body iron homeostasis using a variant of timed Petri nets,” Discret. Appl. Math., vol. 157, no. 10, pp. 2221–2231, May 2009.
  20. D. Rahbari, “High Performance Data mining by Genetic Neural Network”, International Journal of Computer Science and Business Informatics, ISSN: 1694-2108 | Vol. 5, No. 1. SEPTEMBER 2013.
  21. D. Rahbari, “Hybrid Evolutionary Game Theory in QoS Routing of Wireless Mesh Networks”, International Journal of Computer Science and Telecommunications, Volume 4, Issue 9, September 2013.
  22. D. Rahbari, “A Novel approach in Classification by Evolutionary Neural Networks”, International Journal of Mechatronics, Electrical and Computer Technology, Vol. 4(10), Jan, 2014, pp. 33-52, ISSN: 2305-0543, P33-52.
  23. D. Rahbari, “ Digital handwritten recognition by optimized neural networks”, International Journal of Computers & Technology (IJCT), Volume 13 No 9, pp 35-43.
  24. D. Rahbari, “ A Novel approach in Classification by Evolutionary Neural Networks”, International Journal of Mechatronics, Electrical and Computer Technology, Vol. 4(10), Jan, 2014, pp. 33-52, ISSN: 2305-0543.
  25. J. S. Medina, M. G. Moreno, N. A. D. Ugarte and E.R.Royo, “Simulation times Vs. Network Size in a Genetic Algorithm Based Urban Traffic Optimization Architecture”.
  26. R. A. Ganiyu, S. O. Olabiyisi, E .O . Omidiora, O. Okediran and O. O. Alo, Modelling and simulation of a multi-phase traffic light controlled T-type junction using timed colored petri nets, AMERICAN JOURNAL OF SCIENTIFIC AND INDUSTRIAL RESEARCH© 2011, Science Huβ, http://www.scihub.org/AJSIR. ISSN: 2153-649X doi:10.5251/ajsir.2011.2.3.428.437.

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