Abstract
The topic of road safety is a crucial problem because it has become nowadays one of the main causes of death, despite the efforts made by the countries trying to improve the roads conditions. When starting a journey, there are different factors, both objective and subjective, that influence on the driving safety. In this work, we apply fuzzy logic to model these subjective considerations. A committee machine that combines the information provided by three fuzzy systems has been generated. Each of these fuzzy systems gives a degree of risk when traveling taking into account fuzzy conditions of three variables: car (age, last check, the wear on brakes and wheels, etc.); driver (tiredness, sleeping time, sight, etc.); and characteristics of the trip (day or night, weather conditions, length, urban or country road, etc). The final system gives not only the degree of risk according to this fuzzy prediction but the degree in which this risk could be decreased if some of the conditions change according to the advice the fuzzy decision system provides, such as, for example, if the driver takes a rest, or if the tyres are changed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bedard, M., Guyatt, G.H., Stones, M.J., Hireds, J.P.: The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. Accident Analysis and Prevention 34, 717–727 (2002)
Brown, I.D.: Driver fatigue. Human Factors 36, 298–314 (1994)
Cafiso, S., Lamm, R., La Cava, G.: Fuzzy model for safety evaluation process of new and old roads. J. Transportation Research 1881, 54–62 (2004)
Chong, M., Abraham, A., Paprzyckil, M.: Traffic Accident Analysis Using Machine Learning Paradigms. Informatica 29, 89–98 (2005)
Chen, Y.L.: Driver personality characteristics related to self reported accident involvement and mobile phone use while driving. Safety Science 45, 823–831 (2007)
Evans, L.: The dominant role of drive behaviour in traffic safety. American Journal Public Health 86, 784–786 (1996)
Farias, G., Santos, M., López, V.: Making decisions on brain tumour diagnosis by soft computing techniques. Soft Computing 14, 1287–1296 (2010)
Horne, J.A., Reyner, L.A.: Sleep related vehicle accidents. British Medical Journal 310, 565–567 (1995)
Imkamon, T., Saensom, P., Tangamchit, P., Pongpaibool, P.: Detection of hazardous driving behaviour using fuzzy logic. In: Proc. of the IEEE ECTI-CON II, pp. 657–660 (2008)
Jayanth, J., Hariharakrishnan, C.V., Suganthi, L.: Fuzzy Clustering of Locations for Degree of Accident Proneness based on Vehicle User Perceptions. Proceedings of World Academy of Science, Engineering and Technology 33, 182–185 (2008)
Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications, ch. 4. Prentice Hall, India (2005)
Kweon, Y.J., Kockelman, D.M.: Overall injury risk to different drivers: combining exposure, frequency and severity models. Accident Analysis and Preventions 35, 441–450 (2003)
Liu, B.: Uncertain Risk Analysis and Uncertain Reliability Analysis. Journal of Uncertain Systems 4(3), 163–170 (2010)
Logan, R.W., Nitta, C.K., Chidester, S.K.: Risk Reduction as the Product of Model Assessed Reliability, Confidence, and Consequence. Journal of Defence Modeling and Simulation 2(4), 191–207 (2005)
Logan, R.W., Nitta, C.K.: Validation, Uncertainty, and Quantitative Reliability at Confidence (QRC). In: AIAA-2003-1337 (2003)
López, V., Santos, M., Montero, J.: Improving Reliability and Performance in Computer Systems by means of Fuzzy Specifications. In: Intelligent Decision Making Systems, pp. 351–356. World Scientific (2009)
López, V., Santos, M., Montero, J.: Fuzzy Specification in Real Estate Market Decision Making. International Journal of Computational Intelligence Systems 3(1), 8–20 (2010)
Miyajima, C., Nishiwaki, Y., Ozawa, K., Wakita, T., Itou, K., Takeda, K., Itakura, F.: Driver modeling based on driving behaviour and its evaluation in driver identification. Proc. IEEE 95(2), 427–437 (2007)
Moore-Ede, M., Heitmann, A., Guttkuhn, R., Trutschel, U., Aguirre, A., Crok, D.: Circadian Alertness Simulator for Fatigue Risk Assessment in Transportation: Application to Reduce Frequency and Severity of Truck Accidents. Aviation, Space, and Environmental Medicine 75(1), A107–A111 (2004)
Petridou, E., Moustaki, M.: Human factors in the causation of road traffic crashes. European Journal of Epidemiology 16, 819–826 (2000)
Shinar, D.: Psychology on the raoad. In: The human factor in traffic safety, pp. 29–40. John Wiley & Sons, USA (1978)
Valverde, L., Santos, M., López, V.: Fuzzy decision system for safety on roads. In: Intelligent Decision Making Systems, World Scientific, pp. 326–331 (2009)
Xiao, J., Kulakowsk, B.T., Ei-Gindy, N.: Prediction of risk of wet-pavement accidents: fuzzy logic model. J. Transportation Research 1717, 28–36 (2000)
Zadeh, L.: The concept of linguistic variable and its application to approximate reasoning, parts I, II, III. Inform. Sci. 8, 199–249; 8, 301–357; 9, 43–80 (1975)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Santos, M., López, V. (2012). Fuzzy Decision System for Safety on Roads. In: Lu, J., Jain, L.C., Zhang, G. (eds) Handbook on Decision Making. Intelligent Systems Reference Library, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25755-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-25755-1_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25754-4
Online ISBN: 978-3-642-25755-1
eBook Packages: EngineeringEngineering (R0)