Table of Contents
International Journal of Vehicular Technology
Volume 2012, Article ID 807805, 12 pages
http://dx.doi.org/10.1155/2012/807805
Research Article

A Neurofuzzy Approach to Modeling Longitudinal Driving Behavior and Driving Task Complexity

Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands

Received 14 September 2012; Revised 20 November 2012; Accepted 4 December 2012

Academic Editor: Motoyuki Akamatsu

Copyright © 2012 R. G. Hoogendoorn et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. M. Violanti and J. R. Marshall, “Cellular phones and traffic accidents: an epidemiological approach,” Accident Analysis and Prevention, vol. 28, no. 2, pp. 265–270, 1996. View at Publisher · View at Google Scholar · View at Scopus
  2. D. A. Redelmeier and R. J. Tibshirani, “Association between cellular-telephone calls and motor vehicl collisions,” The New England Journal of Medicine, vol. 336, no. 7, pp. 453–458, 1997. View at Publisher · View at Google Scholar · View at Scopus
  3. L. Tijerina, E. Parmer, and M. J. Goodman, “Driver workload assessment of rute guidance system destination entry while driving: a test track study,” in Proceedings of the 5th ITS World Congress (CD-ROM), Seoul, South Korea, 1998.
  4. K. A. Brookhuis, G. De Vries, and D. De Waard, “The effects of mobile telephoning on driving performance,” Accident Analysis and Prevention, vol. 23, no. 4, pp. 309–316, 1991. View at Publisher · View at Google Scholar · View at Scopus
  5. M. R. Endsley, “Theoretical underpinnings of situation awareness: a critical review,” in Situation Awareness Analysis and Measurement, 2000. View at Google Scholar
  6. R. Hoogendoorn, S. P. Hoogendoorn, K. Brookhuis, and W. Daamen, “Mental workload, longitudinal driving behavior, and adequacy of car-following models for incidents in other driving lane,” Transportation Research Record, no. 2188, pp. 64–73, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Fuller, “Towards a general theory of driver behaviour,” Accident Analysis and Prevention, vol. 37, no. 3, pp. 461–472, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. D. E. Haigney, R. G. Taylor, and S. J. Westerman, “Concurrent mobile (cellular) phone use and driving performance: task demand characteristics and compensatory processes,” Transportation Research, vol. 3, no. 3, pp. 113–121, 2000. View at Google Scholar · View at Scopus
  9. J. A. Michon, “A critical view of driver behavior models: what do we know, what should we do,” in Human Behavior and Traffic Safety, pp. 485–520, 1985. View at Google Scholar
  10. H. Alm and L. Nilsson, “The effects of a mobile telephone task on driver behaviour in a car following situation,” Accident Analysis and Prevention, vol. 27, no. 5, pp. 707–715, 1995. View at Google Scholar · View at Scopus
  11. M. E. Rakauskas, L. J. Gugerty, and N. J. Ward, “Effects of naturalistic cell phone conversations on driving performance,” Journal of Safety Research, vol. 35, no. 4, pp. 453–464, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. A. H. Jamson, S. J. Westerman, G. R. J. Hockey, and O. M. J. Carsten, “Speech-based e-mail and driver behavior: effects of an in-vehicle message system interface,” Human Factors, vol. 46, no. 4, pp. 625–639, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. D. L. Strayer, F. A. Drews, and W. A. Johnston, “Cell phone-induced failures of visual attention during simulated driving,” Journal of Experimental Psychology, vol. 9, no. 1, pp. 23–32, 2003. View at Google Scholar · View at Scopus
  14. H. Makishita and K. Matsunaga, “Differences of drivers' reaction times according to age and mental workload,” Accident Analysis and Prevention, vol. 40, no. 2, pp. 567–575, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. W. B. Cannon, Bodily Changes in Pain, Hunger, Fear, and Rage, D. Appleton and Company, 1915.
  16. G. Matthews, L. Dorn, T. W. Hoyes, D. R. Davies, A. I. Glendon, and R. G. Taylor, “Driver stress and performance on a driving simulator,” Human Factors, vol. 40, no. 1, pp. 136–149, 1998. View at Publisher · View at Google Scholar · View at Scopus
  17. R. G. Taylor, “Personality traits and discrepant achievement: a review,” Journal of Counseling Psychology, vol. 11, no. 1, pp. 76–82, 1964. View at Publisher · View at Google Scholar · View at Scopus
  18. D. C. Gazis, R. Herman, and R. W. Rothery, “Analytical methods in transportation: mathematical car-following theory of traffic flow,” Journal of the Engineering Mechanics, vol. 89, no. 6, pp. 29–46, 1963. View at Google Scholar
  19. M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical Review E, vol. 62, no. 2, pp. 1805–1824, 2000. View at Google Scholar · View at Scopus
  20. M. Bando, K. Hasebe, K. Nakanishi, A. Nakayama, A. Shibata, and Y. Sugiyama, “Phenomenological study of dynamical model of traffic flow,” Journal De Physique I, vol. 5, no. 11, pp. 1389–1399, 1995. View at Google Scholar
  21. W. Leutzbach and R. Wiedemann, “Development and applications of traffic simulation models at the karlsruhe institut fuer verkehrswesen,” Traffic Engineering and Control, vol. 27, no. 5, pp. 270–275, 1986. View at Google Scholar · View at Scopus
  22. A. Kesting, M. Treiber, and D. Helbing, “Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity,” Philosophical Transactions of the Royal Society A, vol. 368, no. 1928, pp. 4585–4605, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. C. Tampere, Arem, B. van, and S. P. Hoogendoorn, “Gas-kinetic traffic flow modeling including continuous driver behavior models,” in Proceedings of the Annual Meeting of the Transportation Research Board, Washington, DC, USA, 2003.
  24. R. G. Hoogendoorn, B. Van Arem, S. P. Hoogendoorn, and K. A. Brookhuis, “Applying the task-capability-interface model to the intelligent driver model in relation to complexity,” in Proceedings of the Annual Meeting of the Transportation Research Board, Washington, DC, USA, 2013.
  25. R. G. Hoogendoorn, Empirical Research and Modeling of Longitudinal Driving Behavior Under Adverse Conditions, TRAIL, 2012.
  26. T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116–132, 1985. View at Google Scholar · View at Scopus
  27. H. Bersini and G. Bontempi, “Now comes the time to defuzzify neuro-fuzzy models,” Fuzzy Sets and Systems, vol. 90, no. 2, pp. 161–169, 1997. View at Google Scholar · View at Scopus
  28. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  29. R. Babuska and H. B. Verbruggen, “Fuzzy set methods for local modeling and identification,” in Multiple Model Approaches to Modeling and Control, R. Murray-Smith and T. A. Johansen, Eds., pp. 75–100, Taylor & Francis, 1997. View at Google Scholar
  30. R. Babuska, Fuzzy modeling and identification [Ph.D. thesis], Technische Universiteit Delft, Delft, The Netherlands, 1996.
  31. J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Matlab Curriculum Series, Prentice Hall, 1997.
  32. M. Stone, “Cross validatory choice and assessment of statistical predictions,” Journal of the Royal Statistical Society B, vol. 36, no. 1, pp. 111–147, 1974. View at Google Scholar