Vehicle Safety Device (Airbag) Specific Classification of Road Traffic Accident Patterns through Data Mining Techniques

  • S. Shanthi
  • R. Geetha Ramani
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


Rich developing countries suffer from the consequences of increase in both human and vehicle population. Road accident fatality rates depend upon many factors which could vary for different countries. It is a very challenging task and investigating the dependencies between the attributes become complex because of many environmental and road related factors. In this research work we applied data mining classification technique RndTree and RndTree using ensemble methods viz. Bagging, AdaBoost and Multi Cost Sensitive Bagging (MCSB) to carry out vehicle safety device based classification of which RndTree using Adaboost gives high accurate results. The training dataset used for the research work is obtained from Fatality Analysis Reporting System (FARS) which is provided by the University of Alabama’s Critical Analysis Reporting Environment (CARE) system. The results reveal that RndTree using Adaboost improvised the classifier’s accuracy.


Data Mining Classification Algorithms Bagging Boosting MCSB Road Accident Data RndTree 


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  1. 1.
    Bagging and Boosting,
  2. 2.
    Paul, B., Athithan, G., Narasimha Murty, M.: Speeding up AdaBoost classifier with random projection. In: Seventh International Conference on Advances in Pattern Recognition, pp. 251–254 (2009)Google Scholar
  3. 3.
    Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36, 105–139 (1999)CrossRefGoogle Scholar
  4. 4.
    Han, J., Kamber, M.: Data mining: concepts and techniques. Academic Press, ISBN 1- 55860-489-8Google Scholar
  5. 5.
    Thongkam, J., Xu, G., Zhang, Y.: AdaBoost algorithm with random forests for predicting breast cancer survivability. In: International Joint Conference on Neural Networks (2008)Google Scholar
  6. 6.
    Wen, J., Zhang, X., Xu, Y., Li, Z., Liu, L.: Comparison of AdaBoost and logistic regression for detecting colorectal cancer patients with synchronous liver metastasis. In: International Conference on Biomedical and Pharmaceutical Engineering, December 2-4 (2009)Google Scholar
  7. 7.
    Kuncheva, L.I., Skurichina, M., Duin, R.P.W.: An experimental study on diversity for bagging and boosting with linear classifiers. Information fusion, Science Direct 3(4), 245–258Google Scholar
  8. 8.
    Breiman, L.: Arcing Classifiers. The Annals of Statistics 26, 801–849 (1998)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Miao, Z., Pan, Z., Hu, G., Zhao, L.: Treating missing data processing based on neural network and AdaBoost. In: IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, China, November 18-20 (2007)Google Scholar
  10. 10.
    Seliya, N., Khoshgoftaar, T.M., Van Hulse, J.: A study on the relationships of classifier performance metrics. In: IEEE International Conference on Tools with Artificial Intelligence, pp. 59–66 (2009)Google Scholar
  11. 11.
    Random Tree Algorithm,
  12. 12.
  13. 13.
    Shanthi, S., Geetha Ramani, R.: Classification of Vehicle Collision Patterns in Road Accidents using Data Mining Algorithms. Int. Journal of Computer Applications 35(12), 30–37 (2011)Google Scholar
  14. 14.
    Dietterich, T.G.: An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning 40, 139–157 (2000)CrossRefGoogle Scholar
  15. 15.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139 (1997)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    World Health Organization: Global status report on road safety: time for action, Geneva (2009)Google Scholar
  17. 17.
    FARS analytic reference guide,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringRajalakshmi Institute of Technology, KuthambakkamChennaiIndia
  2. 2.Department of Computer Science and EngineeringRajalakshmi Engineering College, ThandalamChennaiIndia

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