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).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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.
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.
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.
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.
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.
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.
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.
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.
Chimba, D., & Sando, T. (2009). The prediction of highway traffic accident injury severity with neuromorphic techniques. Advances in Transportation Studies, 2009(19), 17–26.
Chong, M. M., Abraham, A., & Paprzycki, M. (2004). Traffic accident analysis using decision trees and neural networks. arXiv preprint cs/0405050.
Chong, M., Abraham, A., & Paprzycki, M. (2005). Traffic accident analysis using machine learning paradigms. Informatica, 29(1).
Crammer, K., & Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2(Dec), 265–292.
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.
Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378.
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, pp. 337–387). New York: Springer series in statistics.
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.
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.
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.
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.
Mishra, S. (2017). Handling imbalanced data: SMOTE vs. random undersampling.
Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1–17.
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.
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.
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.
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.
Weiss, G. M., & Provost, F. (2001). The effect of class distribution on classifier learning: An empirical study. Rutgers University.
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.
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).
Zeng, Q., & Huang, H. (2014). A stable and optimized neural network model for crash injury severity prediction. Accident Analysis and Prevention, 73, 351–358.
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-10374-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-10373-6
Online ISBN: 978-3-030-10374-3
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)