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

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Laser Scanning Systems in Highway and Safety Assessment

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).

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References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Akguuml, A. P., & DoÄŸan, 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Chong, M. M., Abraham, A., & Paprzycki, M. (2004). Traffic accident analysis using decision trees and neural networks. arXiv preprint cs/0405050.

    Google Scholar 

  • Chong, M., Abraham, A., & Paprzycki, M. (2005). Traffic accident analysis using machine learning paradigms. Informatica, 29(1).

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378.

    Article  MathSciNet  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mishra, S. (2017). Handling imbalanced data: SMOTE vs. random undersampling.

    Google Scholar 

  • Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1–17.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Weiss, G. M., & Provost, F. (2001). The effect of class distribution on classifier learning: An empirical study. Rutgers University.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Zeng, Q., & Huang, H. (2014). A stable and optimized neural network model for crash injury severity prediction. Accident Analysis and Prevention, 73, 351–358.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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Correspondence to Biswajeet Pradhan .

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Pradhan, B., Ibrahim Sameen, M. (2020). Predicting Injury Severity of Road Traffic Accidents Using a Hybrid Extreme Gradient Boosting and Deep Neural Network Approach. In: Laser Scanning Systems in Highway and Safety Assessment. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-10374-3_10

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