Incorporating driver distraction in car-following models: Applying the TCI to the IDM

ITS can play a significant role in the improvement of traffic flow, traffic safety and greenhouse gas emissions. However, the implementation of Advanced Driver Assistance Systems may lead to adaptation effects in longitudinal driving behavior following driver distraction. It was however not yet clear how to model these adaptation effects in driving behavior mathematically and on which theoretical framework this should be grounded. To this end in this contribution we introduce a theoretical framework based on the Task-Capability-Interface model by Fuller and integrate this model into the Intelligent Driver Model. Through a case study using simulations we show that this integration provides a relatively adequate description of the effects of driver distraction. The contribution finishes with conclusions and recommendations for future research.

[1]  Katsuya Matsunaga,et al.  Differences of drivers' reaction times according to age and mental workload. , 2008, Accident; analysis and prevention.

[2]  R. Fuller.  Towards a general theory of driver behaviour. , 2005, Accident; analysis and prevention.

[3]  Jan Törnros,et al.  Mobile phone use – effects of conversation on mental workload and driving speed in rural and urban environments , 2006 .

[4]  Dirk Helbing,et al.  Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[5]  Mike McDonald,et al.  Car-following: a historical review , 1999 .

[6]  Y. Sugiyama,et al.  Phenomenological Study of Dynamical Model of Traffic Flow , 1995 .

[7]  W Leutzbach,et al.  DEVELOPMENT AND APPLICATIONS OF TRAFFIC SIMULATION MODELS AT THE KARLSRUHE INSTITUT FUR VERKEHRWESEN , 1986 .

[8]  W J Horrey,et al.  Dissociation between driving performance and drivers' subjective estimates of performance and workload in dual-task conditions. , 2009, Journal of safety research.

[9]  Walter B. Cannon.  The general organization of the visceral nerves concerned in emotions. , 1915 .

[10]  E. Boer.  Car following from the driver’s perspective , 1999 .

[11]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[12]  Michael J. Goodman,et al.  NHTSA DRIVER DISTRACTION RESEARCH: PAST, PRESENT, AND FUTURE , 2001 .

[13]  M. Washburn,et al.  Bodily Changes in Pain, Hunger, Fear, and Rage. , 1917 .

[14]  D. de Waard,et al.  Assessment of drivers' workload: performance, subjective and physiological indices , 2001 .

[15]  W. H. Janssen,et al.  Behavior and safety when driving with in-vehicle devices that provide real-time traffic information , 2000 .

[16]  R. G. Hoogendoorn,et al.  Empirical research and modeling of longitudinal driving behavior under adverse conditions. Thesis Delft University of Technology. , 2012 .

[17]  Serge P. Hoogendoorn,et al.  Capacity Reduction at Incidents , 2008 .

[18]  B. van Arem,et al.  Gas-Kinetic Traffic Flow Modeling Including Continuous Driver Behavior Models , 2002 .

[19]  Ronald G. Taylor.  Personality traits and discrepant achievement: A review. , 1964 .

[20]  Victor L. Knoop,et al.  Integrated Lane Change Model with Relaxation and Synchronization , 2012 .