Accident Severity Prediction Using Data Mining Methods

Authors

  • S. Ramya  B.Tech Student, Department of CSE, Vasireddy Venkatadri Institute of Technology, Namburu, Andhra Pradesh, India
  • SK. Reshma  B.Tech Student, Department of CSE, Vasireddy Venkatadri Institute of Technology, Namburu, Andhra Pradesh, India
  • V. Dhatri Manogna  B.Tech Student, Department of CSE, Vasireddy Venkatadri Institute of Technology, Namburu, Andhra Pradesh, India
  • Y. Satya Saroja  B.Tech Student, Department of CSE, Vasireddy Venkatadri Institute of Technology, Namburu, Andhra Pradesh, India
  • Dr. G. Sanjay Gandhi  Professor, Department of CSE, Vasireddy Venkatadri Institute of Technology, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT195293

Keywords:

Decision Making, Accidents Severity Prediction, Random Forest Algorithm.

Abstract

The smart city concept provides opportunities to handle urban problems, and also to improve the citizens’ living environment. In recent years, road traffic accidents (RTAs) have become one of the largest national health issues in the world and it is leading cause for deaths. The burden of road accident casualties and damage is much higher in developing countries than in developed countries. Many factors (driver, environment, vehicle, etc.) are related to traffic accidents, some of those factors are more important in determining the accident severity than others. The analytical data mining solutions can significantly be employed to determine and predict such influential factors among human, vehicle and environmental factors. In this research, the classification technique i.e., Random forest algorithm is used to identify relevant patterns and for classifying the type of accident severity of various traffic accidents with the help of influential environmental features of RTAs that can be used to build the prediction model. This technique was tested using a real dataset. A decision system has been built using the model generated by the Random Forest technique that will help decision makers to enhance the decision making process by predicting the severity of the accident.

References

  1. Abdel-Aty, M., and Abdelwahab, H. 2003,“Analysis and Prediction of Traffic Fatalities Resulting From Angle Collisions Including the Effect of Vehicles’ Configuration and Compatibility”. Accident Analysis and Prevention.
  2. Akomolafe, T., and Olutayo, A. 2012. “Using Data Mining Technique to Predict Cause of Accident and Accident Prone Locations on Highways.” American Journal of Database Theory and Application 1 (3): 26-38.
  3. Al-Masaeid, H.R., (2009). Traffic accidents in Jordan. Jordan Journal of Civil Engineering, 3(4), pp.331-343.
  4. Al-Zubi, A. S. A., (2010). Analysis of Vehicles Accidents in Amman City Using Spatial Data Mining and Visualization (Doctoral dissertation, The University Of Jordan).
  5. Beshah, T. and Hill, S., (2010), Mining Road Traffic Accident Data to Improve Safety: Role of Road-Related Factors on Accident Severity in Ethiopia. In AAAI Spring Symposium: Artificial Intelligence for Development.
  6. Beshah, T., A. Abraham, et al. (2005). "Rule Mining and Classification of Road Traffic Accidents Using Adaptive Regression Trees." Journal of Simulation 6(10-11).
  7. Beshah, T., Ejigu, D., Abraham, A., Snasel, V. and Kromer, P., (2013). Mining Pattern from Road Accident Data: Role of Road Users Behaviour and Implications for improving road safety. International Journal of Tomography and Simulation, 22(1), pp.73-86.
  8. Chang, L.Y., Wang, H.W., 2006. Analysis of traffic injury severity: an application of non-parametric classification tree techniques. Accident Analysis and Prevention 38, 1019–1027.
  9.  De Ona, ˜ J., Mujalli, R.O., Calvo, F.J., 2011. Analysis of traffic accident injury on Spanish rural highways using Bayesian networks. Accident Analysis and Prevention 43, 402–411.
  10. Effati, M., Rajabi, M.A., Hakimpour, F. and Shabani, S., (2014). Prediction of crash severity on two-lane, two-way roads based on fuzzy classification and regression tree using geospatial analysis. Journal of Computing in Civil Engineering, 29(6), p.04014099.
  11. El Tayeb, A. A., Pareek, V., Araar, A., (2015). Applying Association Rules Mining Algorithms for Traffic Accidents in Dubai. International Journal of Soft Computing and Engineering (IJSCE), 5(4).
  12. Helai, H., Chor, C.H., Haque, M.M., 2008. Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis. Accident Analysis and Prevention 40, 45–54.
  13. Jadaan, K.S., Al-Fayyad, M. and Gammoh, H.F., (2014). Prediction of Road Traffic Accidents in Jordan using Artificial Neural Network (ANN). Journal of Traffic and Logistics Engineering, 2(2).
  14. Krishnaveni, S., and Hemalatha M., (2011). A perspective analysis of traffic accident using data mining techniques. International Journal of Computer Applications, 23(7), pp.40-48.
  15. Kunt, M.M., Aghayan, I. and Noii, N., (2011). Prediction for traffic accident severity: comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods. Transport, 26(4), pp.353-366.
  16. Mohamed, E.A., (2014). Predicting Causes of Traffic Road Accidents Using Multi-class Support Vector Machines. In Proceedings of the International Conference on Data Mining (DMIN) (p. 1).
  17. Obaidat, M.T., and Ramadan, T.M., (2012). Traffic accidents at hazardous locations of urban roads. Jordan Journal of Civil Engineering, 6(4), pp.436-447.
  18. Olutayo, V.A., and Eludire, A.A., (2014). Traffic Accident Analysis Using Decision Trees and Neural Networks. International Journal of Information Technology and Computer Science (IJITCS), 6(2), p.22.
  19. Ossenbruggen, P.J., Pendharkar, J. and Ivan, J.2001, “Roadway safety in rural and small urbanized areas”. Accidents Analysis and Prevention, 33 (4), pp. 485– 498.
  20. Perone, C.S., (2015). Injury risk prediction for traffic accidents in Porto Alegre/RS, Brazil. arXiv preprint arXiv:1502.00245.
  21. Prediction Based on Neural Network.” Presented at the 2nd International Conference on DigitHuilin, F., and Yucai, Z. 2011. “The Traffic Accidental Manufacturing & Automation.
  22. Sohn, S. and S. Hyungwon (2001). "Pattern recognition for a road traffic accident severity in Korea." Ergonomics 44(1): 101-117.
  23. Srisuriyachai, S. (2007). Analysis of road traffic accidents in NakhonPathom province of Bangkok using data mining. Graduate Studies. Bangkok, Mahidol University.
  24. WondwossenMulugeta. 1999, “Correlates of car traffic accident: the case of Addis Ababa in 1990”. Addis Ababa University, Addis Ababa.
  25. Yousif, J.H., and AlRababaa, M.S., (2013). Neural Technique for Predicting Traffic Accidents in Jordan. Journal of American Science, 9(11).

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S. Ramya, SK. Reshma, V. Dhatri Manogna, Y. Satya Saroja, Dr. G. Sanjay Gandhi, " Accident Severity Prediction Using Data Mining Methods, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.528-536, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195293