In this chapter, the author presents a general framework classifying the different models adopted for capturing driver behavior focusing on the human cognitive dimensions and the traffic decision-making dimensions. Special interest is directed toward the “lower-level” microscopic models that can be linked directly to two core driving assistance technologies: adaptive cruise controls and lane-departure warning systems. These “lower-level” models are classified either as acceleration models or as lane changing models.

Acceleration models are at the core of operational driving behaviors, and include car-following models which capture interactions between a lead vehicle and following vehicles. The main assumption in these models is that the behavior of the following vehicle is directly related to a stimulus observed/perceived by the driver, defined relative to the lead vehicle. In addition to the operational aspect, lane changing models capture the tactical side of driving. Most lane changing models have followed a deterministic rule-based framework where changing lanes is directly related to the desirability of such maneuver, its necessity, and its possibility/safety. Recognizing the limitations of the major existing microscopic traffic models, the objective in this chapter is to advance the state of knowledge in modeling driver behavioral processes and to offer an insight into current modeling approaches and the corresponding advantages and disadvantages.


Cellular Automaton Model Lane Change Acceleration Model Lead Vehicle Departure Time Choice 
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.


  1. Ahmed KI (1999) Modeling driver’s acceleration and lane changing behaviors. Ph.D. Dissertation, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
  2. Bando M, Hasebe K, Nakayama A, Shibata A, Sugiyama Y (1995) Dynamical model of traffic congestion and numerical simulation. Phys Rev E 51:1035–1042CrossRefGoogle Scholar
  3. Ben-Akiva ME, Lerman SR (1985) Discrete choice analysis. MIT Press, Cambridge MAGoogle Scholar
  4. Boer ER (1999) Car following from the driver’s perspective. Transportation Research Board, Part F 2(4):201–206CrossRefGoogle Scholar
  5. Brackston M, McDonald M (1999) Car-following: a historical review. Transportation Research, Part F 2(4):181–196CrossRefGoogle Scholar
  6. Chandler R, Herman R, Montroll W (1958) Traffic dynamics: studies in car-following. Oper Res 6:165–184MathSciNetCrossRefGoogle Scholar
  7. Edie LC (1961) Car-following and steady-state theory for non-congested traffic. Oper Res 9:66–76.Google Scholar
  8. FHWA (2004) NGSIM Task E.1-1: core algorithms assessment, final report, Cambridge Systematic, Inc., MassachusettsGoogle Scholar
  9. Gazis DC, Herman R, Potts R (1959) Car-following theory of steady state traffic flow. Oper Res 7:499–505MathSciNetCrossRefGoogle Scholar
  10. Gazis D, Herman R, Rothery R (1961) Nonlinear follow-the-leader models of traffic flow. Oper Res 9:545–567MathSciNetMATHCrossRefGoogle Scholar
  11. Gipps PG (1981) A behavioral car-following model for computer simulation. Transportation Research 15B:101–115Google Scholar
  12. Gipps PG (1986) A model for the structure of lane changing decisions. Transportation Research 20B:403–414Google Scholar
  13. Hamdar SH, Mahmassani HS (2008) From existing accident-free car-following models to colliding vehicles: exploration and assessment, National Research Council (US). Transportation Research Board Meeting, Washington, DC, 87th January 2008. Preprint CD-ROMGoogle Scholar
  14. Hastie R, Dawes RM (2001) Rational choice in an uncertain world. Sage, Thousand OaksGoogle Scholar
  15. Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 406:487–491CrossRefGoogle Scholar
  16. Hidas P (2002) Modeling lane-changing and merging in microscopic traffic simulation. Transportation Research, Part C 10(2):351–371CrossRefGoogle Scholar
  17. Kesting A, Treiber M, Helbing D (2007) MOBIL: general lane changing model for car-following models. Transportation Research Record 1999/2007:86–94CrossRefGoogle Scholar
  18. Krauss S, Wagner P (1997) Metastable states in a microscopic model of traffic flow. Phys Rev E 55(5):5597–5602CrossRefGoogle Scholar
  19. Krauss S, Wagner P, Gawron C (1996) Continuous limit of Nagel-Shreckenberg model. Phys Rev E 54(4):3707–3712CrossRefGoogle Scholar
  20. Nagel K, Shreckenberg M (1992) A cellular automaton model for freeway traffic. J Phys I (France) 2:2221–2229CrossRefGoogle Scholar
  21. National Highway Traffic Safety (1998) Aggressive driving; help get the world out, US. DOT. http://purl.access.gpo.gov/GPO/LPS3277. Accessed December, 2004
  22. Newel G (1961) Nonlinear effects in the dynamics of car-following. Oper Res 9:209–229CrossRefGoogle Scholar
  23. Ranney TA (1999) Psychological factors that influence car-following and car-following model development. Transportation Research, Part F 2(4):213–219CrossRefGoogle Scholar
  24. Rothery RW (1999) Traffic flow theory: A state-of-the-Art report-revised monograph on traffic flow theory. Transportation Research Board, National Research Council, Washington, DCGoogle Scholar
  25. Schmidt LJ, Warner B (2002) Panic: origins, insight, and treatment. North Atlantic Books, CaliforniaGoogle Scholar
  26. Schultz DP (1964) Panic behavior, discussion and readings. Random House, New YorkGoogle Scholar
  27. Tadaki S, Nishinari K, Kikuchi M, Sugiyama Y, Yukawa S (2002) Analysis of congested flow at the upper stream of a tunnel. Physica A 315:156–162CrossRefGoogle Scholar
  28. Tilch B, Helbing D (1998) Generalized force model of traffic dynamics. Phys Rev E 58:133CrossRefGoogle Scholar
  29. Treiber M, Hennecke K, Helbing D (2000) Congested traffic states in empirical observations and microscopic simulations. Phys Rev E 2(2):1805–1824CrossRefGoogle Scholar
  30. Treiber M, Kesting A, Helbing D (2006) Delays, inaccuracies and anticipation in microscopic traffic models. Physica A 360:71–88CrossRefGoogle Scholar
  31. Wallsten TS, Pleskac TJ, Lejuez CW (2005) Modeling behavior in a clinically diagnostic sequential risk-taking task. Psychol Rev 112(4):862–880CrossRefGoogle Scholar
  32. Wiedemann R, Reiter U, (1992) Microscopic traffic simulation, the simulation system mission. Project ICARUS (V1052) Final Report, CEC, BrusselsGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.The George Washington UniversityAshburnUSA

Personalised recommendations