Advertisement

Predicting Injury Severity of Road Traffic Accidents Using a Hybrid Extreme Gradient Boosting and Deep Neural Network Approach

  • Biswajeet PradhanEmail author
  • Maher Ibrahim Sameen
Chapter
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Technical tools such as predictive analytics and computational models are essential to forecast future scenarios of road safety. Predictive models are classified into two main groups, namely statistical (e.g., logistic regression) and computational intelligence (e.g., neural network or NN).

References

  1. Abdel-Aty, M. A., & Abdelwahab, H. T. (2004). Predicting injury severity levels in traffic crashes a modeling comparison. Journal of Transportation Engineering, 130(2), 204–210.CrossRefGoogle Scholar
  2. Abellán, J., López, G., & De OñA, J. (2013). Analysis of traffic accident severity using decision rules via decision trees. Expert Systems with Applications, 40(15), 6047–6054.CrossRefGoogle Scholar
  3. Akguuml, A. P., & Doğan, E. (2009). An application of modified Smeed, adapted Andreassen and artificial neural network accident models to three metropolitan cities of Turkey. Scientific Research and Essays, 4(9), 906–913.Google Scholar
  4. Akin, D., & Akbas, B. (2010). A neural network (NN) model to predict intersection crashes based upon driver, vehicle and roadway surface characteristics. Scientific Research and Essays, 5(19), 2837–2847.Google Scholar
  5. Batista, G. E., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20–29.CrossRefGoogle Scholar
  6. Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.CrossRefGoogle Scholar
  7. 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(5), 1019–1027.CrossRefGoogle Scholar
  8. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.CrossRefGoogle Scholar
  9. Chimba, D., & Sando, T. (2009). The prediction of highway traffic accident injury severity with neuromorphic techniques. Advances in Transportation Studies, 2009(19), 17–26.Google Scholar
  10. Chong, M. M., Abraham, A., & Paprzycki, M. (2004). Traffic accident analysis using decision trees and neural networks. arXiv preprint cs/0405050.Google Scholar
  11. Chong, M., Abraham, A., & Paprzycki, M. (2005). Traffic accident analysis using machine learning paradigms. Informatica, 29(1).Google Scholar
  12. Crammer, K., & Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2(Dec), 265–292.Google Scholar
  13. Delen, D., Sharda, R., & Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 38(3), 434–444.CrossRefGoogle Scholar
  14. Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378.MathSciNetCrossRefGoogle Scholar
  15. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, pp. 337–387). New York: Springer series in statistics.Google Scholar
  16. He, H., Bai, Y., Garcia, E. A., & Li, S. (2008, June). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence), (pp. 1322–1328). IEEE.Google Scholar
  17. Karlaftis, M. G., & Vlahogianni, E. I. (2011). Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies, 19(3), 387–399.CrossRefGoogle Scholar
  18. Kunt, M. M., Aghayan, I., & 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), 353–366.CrossRefGoogle Scholar
  19. Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., & Wang, Y. (2017). Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17(4), 818.CrossRefGoogle Scholar
  20. Mishra, S. (2017). Handling imbalanced data: SMOTE vs. random undersampling.Google Scholar
  21. Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1–17.CrossRefGoogle Scholar
  22. Sameen, M. I., & Pradhan, B. (2017a). A two-stage optimization strategy for fuzzy object-based analysis using airborne LiDAR and high-resolution orthophotos for urban road extraction. Journal of Sensors.Google Scholar
  23. Sameen, M. I., & Pradhan, B. (2017b). Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 733–747.  https://doi.org/10.1080/19475705.2016.1265012.CrossRefGoogle Scholar
  24. Sameen, M. I., & Pradhan, B. (2017c). A simplified semi-automatic technique for highway extraction from high-resolution airborne LiDAR data and orthophotos. Journal of the Indian Society of Remote Sensing, 45(3), 395–405.  https://doi.org/10.1007/s12524-016-0610-5.CrossRefGoogle Scholar
  25. Sameen, M. I., Pradhan, B., Shafri, H. Z. M., Mezaal, M. R., & Hamid, H. (2016). Integration of ant colony optimization and object-based analysis for LiDAR data classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 2055–2066.  https://doi.org/10.1109/JSTARS.2017.2650956.CrossRefGoogle Scholar
  26. Weiss, G. M., & Provost, F. (2001). The effect of class distribution on classifier learning: An empirical study. Rutgers University.Google Scholar
  27. Xie, Y., Lord, D., & Zhang, Y. (2007). Predicting motor vehicle collisions using Bayesian neural network models: An empirical analysis. Accident Analysis and Prevention, 39(5), 922–933.CrossRefGoogle Scholar
  28. Yang, H., Wang, Z., Xie, K., Ma, Y., & Zhu, Y. (2018). A deep learning approach to predict severity levels of work zone crashes (No. 18-03042).Google Scholar
  29. Zeng, Q., & Huang, H. (2014). A stable and optimized neural network model for crash injury severity prediction. Accident Analysis and Prevention, 73, 351–358.CrossRefGoogle Scholar
  30. Zeng, Q., Huang, H., Pei, X., & Wong, S. C. (2016). Modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks. Analytic Methods in Accident Research, 10, 12–25.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)University of Technology SydneySydneyAustralia
  2. 2.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)University of Technology SydneySydneyAustralia

Personalised recommendations