Anticipation Based on a Bi-Level Bi-Objective Modeling for the Decision-Making in the Car-Following Behavior

  • Anouer BennajehEmail author
  • Fahem Kebair
  • Lamjed Ben Said
  • Samir Aknine
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)


Safety distance models are in a continuous state of improvement due to their important role in the micro-simulation of traffic, intelligent transport systems and safety engineering models. Indeed, many existing models of car-following behavior do not explicitly consider a real link between the increase of the speed of movement and the guarantee of the road safety. In this context, this paper presents a decision-making model for determining velocity and safety distance values basing-on a bi-level bi-objective modeling and that allows simulation parameters anticipation. In fact, our model addresses drivers that circulate in urban zones with normative behaviors. The first objective of the model is to allow agent drivers to have a smooth transition between acceleration and deceleration behaviors according to the leading vehicle actions. Simultaneously, the model intends, as a second objective, to reduce the circulation time by increasing the speed of movement. Agent technology and Tabu search algorithm are used respectively to model drivers and to find the best solution during decision-making. The paper provides first a theoretical background of the research. Then it describes the agent driver decision-making model and the resolution algorithm. Finally it presents and discusses a first simulation and experimentations.


Car-following behavior Safe distance model Bi-objectives modeling Bi-level modeling Anticipation Making decision Software agent Tabu search algorithm 


  1. 1.
    Arnaud, D., René, M., Sylvain, P., Stéphane, E.: A behavioral multi-agent model for road traffic simulation. Eng. Appl. Artif. Intell. 21, 1443–1454 (2008)CrossRefGoogle Scholar
  2. 2.
    Bajeh, A.O., Abolarinwa, K.O.: Optimization: a comparative study of genetic and tabu search algorithms. Int. J. Comput. Appl. (0975–8887) 31(5) (2011)Google Scholar
  3. 3.
    Barcelo, J., Ferrer, J., Grau, R., Florian, M., Chabini, E.: A route based version of the AIMSUN2 micro-simulation model. 2nd World Congree on ITS, Yokohama. (1995)Google Scholar
  4. 4.
    Brackstone, M., Mcdonald, M.: Car-following: a historical review [J]. Transp. Res. Part F: Traffic Psychol. Behav. 2(4), 181–196 (1999)Google Scholar
  5. 5.
    Chen, Y.-L., Wang, C.-A.: Vehicle Safety Distance Warning System: A Novel Algorithm for Vehicle Safety Distance Calculating Between Moving Cars. 1550-2252/$25.00 ©16 IEEE. (2007)Google Scholar
  6. 6.
    Gipps, P.G.: A behavioural car following model for computer simulation. Transp. Res. B 15(2), 105–111 (1981)CrossRefGoogle Scholar
  7. 7.
    Glover, F.: Tabu search-part I. ORSA J. Comput. 1(3) (1989). 0899-1499/89/0103-0190 $01.25.Google Scholar
  8. 8.
    ITE: Transportation and traffic engineering handbook. In: 2nd Edition, Institute of Transportation Engineers. Prentice-Hall, Inc. New Jersey (1982)Google Scholar
  9. 9.
    Kometani, E., Sasaki, T.: Dynamic behaviour of traffic with a nonlinear spacing-speed relationship. In: Proceedings of the Symposium on Theory of Traffic Flow, Research Laboratories, General Motors, pp. 105–119. New York (1959)Google Scholar
  10. 10.
    Liu, R., Van, V.D., Wating, D.P.: DRACULA: dynamic route assignment combining user learning and microsimulation. In: Proceedings of PTRC Summer Annual Conference, Seminar E, pp. 143–152 (1995)Google Scholar
  11. 11.
    Qiang, L., Lunhui, X., Zhihui, C., Yanguo, H.: Simulation analysis and study on car-following safety distance model based on braking process of leading vehicle. In: IEEE Proceedings of the 8th World Congress on Intelligent Control and Automation (2011). 978-1-61284-700-9/11/$26.00 ©2011Google Scholar
  12. 12.
    Wilson, R.E.: An analysis of Gipps’s car-following model of highway traffic. IMA J. Appl. Math. 66, 509–537 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)CrossRefGoogle Scholar
  14. 14.
    Yang, D., Zhu, L.L., Yu, D., Yang, F., Pu, Y.: An enhanced safe distance car-following model. J. Shanghai Jiaotong Univ. (Sci.) 19(1), 115–122 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Anouer Bennajeh
    • 1
    Email author
  • Fahem Kebair
    • 1
  • Lamjed Ben Said
    • 1
  • Samir Aknine
    • 2
  1. 1.SOIE, Institut Supérieur de Gestion de Tunis - ISGTUniversité de TunisBardo – TunisTunisie
  2. 2.LIRIS - Université Claude Bernard Lyon 1 - UCBLVilleurbanne CedexFrance

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