Fuzzy Decision System for Safety on Roads

  • Matilde Santos
  • Victoria López
Part of the Intelligent Systems Reference Library book series (ISRL, volume 33)


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.


Fuzzy Logic Fuzzy System Risk Function Road Type Fuzzy Output 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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)CrossRefGoogle Scholar
  2. Brown, I.D.: Driver fatigue. Human Factors 36, 298–314 (1994)Google Scholar
  3. 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)Google Scholar
  4. Chong, M., Abraham, A., Paprzyckil, M.: Traffic Accident Analysis Using Machine Learning Paradigms. Informatica 29, 89–98 (2005)Google Scholar
  5. Chen, Y.L.: Driver personality characteristics related to self reported accident involvement and mobile phone use while driving. Safety Science 45, 823–831 (2007)CrossRefGoogle Scholar
  6. Evans, L.: The dominant role of drive behaviour in traffic safety. American Journal Public Health 86, 784–786 (1996)CrossRefGoogle Scholar
  7. Farias, G., Santos, M., López, V.: Making decisions on brain tumour diagnosis by soft computing techniques. Soft Computing 14, 1287–1296 (2010)CrossRefGoogle Scholar
  8. Horne, J.A., Reyner, L.A.: Sleep related vehicle accidents. British Medical Journal 310, 565–567 (1995)CrossRefGoogle Scholar
  9. 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)Google Scholar
  10. 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)Google Scholar
  11. Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications, ch. 4. Prentice Hall, India (2005)Google Scholar
  12. 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)CrossRefGoogle Scholar
  13. Liu, B.: Uncertain Risk Analysis and Uncertain Reliability Analysis. Journal of Uncertain Systems 4(3), 163–170 (2010)Google Scholar
  14. 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)CrossRefGoogle Scholar
  15. Logan, R.W., Nitta, C.K.: Validation, Uncertainty, and Quantitative Reliability at Confidence (QRC). In: AIAA-2003-1337 (2003)Google Scholar
  16. 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)Google Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)Google Scholar
  20. Petridou, E., Moustaki, M.: Human factors in the causation of road traffic crashes. European Journal of Epidemiology 16, 819–826 (2000)CrossRefGoogle Scholar
  21. Shinar, D.: Psychology on the raoad. In: The human factor in traffic safety, pp. 29–40. John Wiley & Sons, USA (1978)Google Scholar
  22. Valverde, L., Santos, M., López, V.: Fuzzy decision system for safety on roads. In: Intelligent Decision Making Systems, World Scientific, pp. 326–331 (2009)Google Scholar
  23. 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)Google Scholar
  24. 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)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matilde Santos
    • 1
  • Victoria López
    • 1
  1. 1.Facultad de InformáticaUniversidad Complutense de MadridMadridSpain

Personalised recommendations