JACIII Vol.12 No.2 pp. 172-181
doi: 10.20965/jaciii.2008.p0172


Fuzzy Logic Based Lane Change Model for Microscopic Traffic Flow Simulation

Madhu Errampalli*, Masashi Okushima**, and Takamasa Akiyama**

*Graduate School of Engineering, Gifu University

**Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan

March 15, 2007
July 20, 2007
March 20, 2008
fuzzy reasoning, lane change model, traffic simulation, driver behaviour
Lane changing phenomenon is vital in representing individual vehicle behaviour in microscopic traffic simulation, yet many lane change models do not consider the uncertainties and perceptions in human behaviour that are involved in modelling lane changing. In the present study, fuzzy reasoning in lane changing model is introduced to reflect these uncertainties and perceptions to represent lane changing behaviour more realistically. The comparison of simulated results with observed data indicated that fuzzy reasoning represents driver behaviour more realistically than standard modelling. The effectiveness of the proposed technique is demonstrated in a real urban network with bus lane policy.
Cite this article as:
M. Errampalli, M. Okushima, and T. Akiyama, “Fuzzy Logic Based Lane Change Model for Microscopic Traffic Flow Simulation,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.2, pp. 172-181, 2008.
Data files:
  1. [1] R. M. Pendyala, R. Kitamura, C. Chen, and E. I. Pas, “An Activitybased Microsimulation Analysis of Transportation Control Measures,” Transport Policy, Vol.4, No.3, pp. 183-192, 1997.
  2. [2] J. Barcelo and J. Casas, “Dynamic Network Simulation with AIMSUN,” Proc. Intl. Sym. on Transport Simulation, Japan, 2002.
  3. [3] J. Wu, M. Brackstone, and M. McDonald, “Fuzzy Sets and Systems for A Motorway Microscopic Simulation Model,” Fuzzy Sets and Systems, Vol.116, pp. 65-76, 2000.
  4. [4] P. Hidas, “Modelling Lane Changing and Merging in Microscopic Traffic Simulation,” Transportation Research C, Vol.10, pp. 351-371, 2002.
  5. [5] P. G. Gipps, “A Model for the Structure of Lane-changing Decisions,” Transportation Research B, Vol.35(5), pp. 107-120, 1986.
  6. [6] Q. Yang and H. S. Koutsopoulos, “A Microscopic Traffic Simulator for Evaluation of Dynamic Traffic Management Systems,” Transportation Research C, Vol.4, No.3, pp. 113-129, 1996.
  7. [7] M. Errampalli, M. Okushima, and T. Akiyama, “Microscopic Simulation Model Considering Public Transport Policy,” Journal of Eastern Asia Society for Transport Studies (EASTS), Vol.6, pp. 2718-2733, 2005.
  8. [8] M. Errampalli, M. Okushima, and T. Akiyama, “Evaluation of Bus Lane Policy by Microscopic Simulation Model,” Proc. 4th ITS Symposium, ITS Japan, pp. 187-192, 2005.
  9. [9] Y. Takihi, M. Okushima, and T. Akiyama, “Application of Inflow Control Method with Fuzzy Logic for Urban Expressway,” Proc. of SICS & ISIS, CD-ROM No.20611, 2002.
  10. [10] B. J. Kim, “Design of fuzzy PD+I controller for tracking control,” Proc. of American Control Conference, pp. 2124-2129, 2002.
  11. [11] S. Kikuchi and P. Chakroborty, “Car-following model based on a fuzzy inference system,” TRR 1365, TRB, pp. 82-91, 1992.
  12. [12] M. Errampalli, M. Okushima, and T. Akiyama, “Evaluation of Bus Priority System by Microscopic Simulation Model,” Infrastructure Review, Vol.23, No.4, JSCE, pp. 945-953, 2006.
  13. [13] P. Chakroborty and S. Kikuchi, “Evaluation of General Motors based Car-following Models and Proposed Fuzzy Inference Model,” Transportation Research C, Vol.7, pp. 209-235, 1999.
  14. [14] J. Wu, M. Brackstone, and M. McDonald, “The Validation of a Microscopic Simulation Model: A Methodological Case Study,” Transportation Research C, Vol.11, pp. 463-479, 2003.
  15. [15] L. A. Zadeh, “Fuzzy Sets as a basis for a Theory of Possibility,” Fuzzy Sets and Systems, Vol.100 Supplement, pp. 9-34, 1999.
  16. [16] D. Dubios and H. Prade, “Fuzzy Sets and Systems: Theory and Applications,” Academic Press Inc., New York, 1980.
  17. [17] D. Dubios and H. Prade, “Ranking Fuzzy Numbers in the Setting of Possibility Theory,” Information Sciences, Vol.30, pp. 183-224, 1983.
  18. [18] T. Akiyama, “Extended Traffic Assignment Models with Fuzzy Travel Time,” Proc. 4th AFSS, Japan, Vol.1, May-Jun., pp. 587-592, 2000.
  19. [19] T. Akiyama and T. Nomura, “The Proposal of Fuzzy Traffic Assignment Models,” Journal of EASTS, Vol.3(6), pp. 263-277, 1999.
  20. [20] K. Mizutani and T. Akiyama, “A Descriptive Hybrid Model of Modal Choice using Fuzzy Reasoning,” Proc. 4th AFSS, 2000, Vol.1, May-Jun., pp. 593-598, 2000.
  21. [21] H. P. Tai, T. Akiyama, and M. Okushima, “Development of Combined Modal Split/Traffic Assignment Model with Fuzzy Logic,” Proc. EASTS, Vol.4, pp. 663-677, 2003.
  22. [22] K. Tanaka, “An Introduction to Fuzzy Logic for Practical Applications,” Springer-Verlag, New York, 1997.
  23. [23] H. J. Zimmermann, “Fuzzy Set Theory - and its Applications,” Kluwer Nijhoff Publishing, Hingham, USA, 1985.

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