Knowledge Based Agent for Intelligent Traffic Light Control – An Indian Perspective

  • V. Mandava
  • P. Nimmagadda
  • T. R. Korrapati
  • K. R. Anne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6839)

Abstract

In this paper we have adapted an agent approach for traffic light control. According to this, the proposed system contains agents and their world which in turn contains roads, cars, traffic lights etc. Each of these agents observe the traffic density and control the traffic light at the junction by using observe-think-act rule i.e. the agents will continuously observe the traffic and depending on the density and waiting time it decides which rule can be inferred and finally it implements the condition to the traffic light controller which can efficiently manage the traffic flow near the junction. The system has been implemented by using NetLogo based traffic simulator. The investigation is to control the traffic at the road junction by applying few inference rules.It reduces the waiting time and increases the efficiency of the traffic light controller intelligently.

Keywords

Traffic Flow Traffic Condition Intelligent Transportation System Average Wait Time Agent Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Boggavarapu, L.N.P., Vaddi, S., Munagala, J.K., Anne, K.R.: Localization of Non-standard Licence Plate Using Morphological Operations - An Indian contextGoogle Scholar
  2. 2.
    Wey, W.-M.: Applications of Linear Systems Controller to A Cycle-based Traffic Signal Control. In: 2001 IEEE Intelligent Transportation Systems Conference Proceedings, Oakland (CA), USA, August 25-29 (2001)Google Scholar
  3. 3.
    Cano, M.-D., Cerdan, F., Garcia-Haro, J., Malgosa-Sanahuja, J.: Performance Analysis of the Counters-Based Modified Traffic Conditioner in a DiffServ. NetworkGoogle Scholar
  4. 4.
    Silvani, X., Morandini, F.: An Intrusive Sensor System for Experimental Investigations of Free Wildland FiresGoogle Scholar
  5. 5.
    Kleismit, R.A., Kazimierczuk, M.K.: Evanescent Microwave Sensor Scanning for Detection of Sub-surface Defects in WiresGoogle Scholar
  6. 6.
    Agarwal, V., VenkataMurali, N., Chandramouli, C.: A Cost-Effective Ultrasonic Sensor-Based Driver-Assistance System for Congested Traffic Conditions. IEEE Transactions on Intelligent Transportation Systems 10(3) (September 2009)Google Scholar
  7. 7.
    Lit, Z., Zhong, L.: An Efficient Framework for Detecting Moving Objects and Structural Lane in Video Based Surveillance Systems, 978-1-4244-5228-6/09/$26.00 ©2009 IEEEGoogle Scholar
  8. 8.
    Nakamiti., G., Gomide, F.: Fuzzy Sets in Distributed Traffic Control. In: Proc. 5th IEEE Int. Conf. Fuzzy Systems, pp. 1617–1623 (1996)Google Scholar
  9. 9.
    Chiu., S., Chand, S.: Self-organizing Traffic Control Via Fuzzy Logic. In: Proc. 32nd IEEE Conf. Decision Control, pp. 1987–1902 (1993)Google Scholar
  10. 10.
    Xiao Xiong, W., Shu Shen, Y., Xue Feng, Z.: Architecture of Multi-agent System for Traffic Signal Control. In: Proc. 8th IEEE Conf. Control, Automation, Robotics and Vision, vol. 3, pp. 2199–2204 (2004)Google Scholar
  11. 11.
    Ying., L., Shoufeng., M., Wu., L., Huanchen, W.: Microscopic urban traffic simulation with multi-agent system. In: Proc. of the Joint Conf. of 4th Int. Conf. on Information, Communications and Signal Processing, and 4th Pacific Rim Conf. on Multimedia, pp. 1835–1839 (2003)Google Scholar
  12. 12.
    Hirankitti., V., Krohkaew, J.: An Agent Approach for Intelligent Traffic-Light Control. In: Proc. of Asian Modelling Symposium AMS 2007, pp. 496–501 (2007)Google Scholar
  13. 13.
    Wilensky, U.: NetLogo User Manual version 3.0.2. Center for Connected Learning and Computer-Based Modeling. Northwestern University, Evanston (2005), http://ccl.northwestern.edu/netlogo/ Google Scholar
  14. 14.
    Yang, J.S., Yang, X., Hu, Y., Jiang, Q.: Bending Mode Effect on Sensitivity of Plate Surface Acoustic Wave Pressure SensorsGoogle Scholar
  15. 15.
    Kleismit, R.A., Kazimierczuk, M.K.: Evanescent Microwave Sensor Scanningfor Detection of sub-surface Defects in WiresGoogle Scholar
  16. 16.
    Mikami, S., Kakazu, Y.: Genetic Reinforcement Learning for Cooperative Traffic Signal Control. In: Proc. IEEE World Congress Computational Intelligence (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • V. Mandava
    • 1
  • P. Nimmagadda
    • 2
  • T. R. Korrapati
    • 3
  • K. R. Anne
    • 1
  1. 1.Vellagapudi Ramakrishna Siddhartha Engineering CollegeVijayawadaIndia
  2. 2.P.V.P. Siddhartha Institute of TechnologyVijayawadaIndia
  3. 3.Sridevi Women’s Engineering CollegeHyderabadIndia

Personalised recommendations