Driver Behavior Modeling

  • Samer Hamdar


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.


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Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.The George Washington UniversityAshburnUSA

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