Commenced in January 2007
Paper Count: 30135
Fuzzy Inference Based Modelling of Perception Reaction Time of Drivers
Abstract:Perception reaction time of drivers is an outcome of human thought process, which is vague and approximate in nature and also varies from driver to driver. So, in this study a fuzzy logic based model for prediction of the same has been presented, which seems suitable. The control factors, like, age, experience, intensity of driving of the driver, speed of the vehicle and distance of stimulus have been considered as premise variables in the model, in which the perception reaction time is the consequence variable. Results show that the model is able to explain the impacts of the control factors on perception reaction time properly.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128054Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 588
 Shiffrin, R. and Schneider, W. (1977), Controlled and automatic human information processing II: perceptual learning, automatic attending, and a general theory, Psychological Review, 84, pp. 127–90.
 Green, M. (2000), “How long does it take to stop? Methodological analysis of driver perception-brake times.” Transportation Human Factors ,2, pp.195–216.
 Dabbour, E. and. Easa, S.M. (2009). Perceptual framework for a modern left-turn collision warning system. International Journal of Applied Science, Engineering and Technology, 5(1) pp. 8-14.
 Mehmood, A. and Easa, S. M. (2009). Modeling Reaction Time in Car-Following Behavior Based on Human Factors. World Academy of Science, Engineering and Technology, 57.
 Elander, J., West, R. and French, D. (1993). Behavioral correlates of individual differences in road traffic crash risk: an examination of methods and findings. Psychol. Bull. 113, pp. 279–294.
 Gerlough, D. and Huber, M. (1975) Traffic Flow Theory, TRB special report 165. Technical Report, National Research Council, Washington D. C, U.S.A.
 Hooper, K. G. and McGee, H. W. (1983). Driver Perception Reaction Time: Are Revisions to Current Specifications in Order? Transportation Research Record 904, Transportation Research Board, National Research Council, Ishington, DC, pp. 21-30.
 Chattaraj, U. and Panda, M. (2010), Some Applications of Fuzzy Logic in Transportation Engineering, In Proceedings of International Conference on Challenges and Applications of Mathematics in Science and Technology (CAMIST), NIT Rourkela, pp. 139-148.
 Chakroborty, P. and Kikuchi, S. (2003), Calibrating the Membership Functions of the Fuzzy Inference System: Instantiated by Car-following Data, Transportation Research Part C 11 pp. 91–119.