References
- Abdel-Aty, M. (2003). Analysis of driver injury severity levels at multiple locations using ordered probit models. Journal of Safety Research, 34(5), 597–603. doi:https://doi.org/10.1016/j.jsr.2003.05.009
- 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. doi:https://doi.org/10.1061/(ASCE)0733-947X(2004)130:2(204)
- Abdel-Aty, M., & Abdelwahab, H. (2004). Modeling rear-end collisions including the role of driver’s visibility and light truck vehicles using a nested logit structure. Accident; Analysis and Prevention, 36(3), 447–456. doi:https://doi.org/10.1016/S0001-4575(03)00040-X
- Abdelwahab, H. T., & Abdel-Aty, M. a. (2001). Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board, 1746(1), 6–13. doi:https://doi.org/10.3141/1746-02
- Alkheder, S., Taamneh, M., & Taamneh, S. (2017). Severity prediction of traffic accident using an artificial neural network. Journal of Forecasting, 36(1), 100–108. doi:https://doi.org/10.1002/for.2425
- Behnood, A., & Mannering, F. (2017). Analytic methods in accident research the effect of passengers on driver-injury severities in single-vehicle crashes: A random parameters heterogeneity-in-means approach. Analytic Methods in Accident Research, 14, 41–53. doi:https://doi.org/10.1016/j.amar.2017.04.001
- Behnood, A., & Mannering, F. (2017). Analytic methods in accident research determinants of bicyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 16, 35–47. doi:https://doi.org/10.1016/j.amar.2017.08.001
- Behnood, A., & Mannering, F. (2019). Analytic methods in accident research time-of-day variations and temporal instability of factors affecting injury severities in large-truck crashes. Analytic Methods in Accident Research, 23, 100102. doi:https://doi.org/10.1016/j.amar.2019.100102
- Chang, L. Y., & Chien, J. T. (2013). Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Safety Science, 51(1), 17–22. doi:https://doi.org/10.1016/j.ssci.2012.06.017
- Chong, M., Abraham, A., & Paprzycki, M. (2005). Traffic accident analysis using machine learning paradigms. Informatica, 29, 89–98. https://doi.org/10.5815/ijitcs.2014.02.03.
- De Oña, J., Mujalli, R. O., & Calvo, F. J. (2011). Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accident; Analysis and Prevention, 43(1), 402–411. doi:https://doi.org/10.1016/j.aap.2010.09.010
- Dietterich, T. G., & Dietterich, T. G. (2000). Ensemble methods in machine learning. Proceedings of the First International Workshop on Multiple Classifier Systems, pp. 1–15.
- Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121(2), 256–285. doi:https://doi.org/10.1006/inco.1995.1136
- Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.
- Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011). A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463–484. doi:https://doi.org/10.1109/TSMCC.2011.2161285
- Garrido, R., Bastos, A., Almeida, A. D., & Elvas, J. P. (2014). Prediction of road accident severity using the ordered probit model. Transportation Research Procedia, 3, 214–223. doi:https://doi.org/10.1016/j.trpro.2014.10.107
- Harmon, T., Bahar, G. B., & Gross, F. B. (2018). Crash costs for highway safety analysis (Report No. FHWA-SA-17-071). Federal Highway Administration, Office of Safety, United States.
- Hossin, M., & Sulaiman, M. N. (2015). Review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 1–11. doi:https://doi.org/10.5121/ijdkp.2015.5201
- Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on Artificial intelligence (vol. 2, pp. 1137–1143).
- Krull, K., Khattak, A., & Council, F. (2000). Injury effects of rollovers and events sequence in single-vehicle crashes. Transportation Research Record: Journal of the Transportation Research Board, 1717(1), 46–54. doi:https://doi.org/10.3141/1717-07
- 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. doi:https://doi.org/10.3846/16484142.2011.635465
- Lee, C., Abdel-Aty, M., Park, J., & Wang, J. (2015). Development of Crash modification factors for changing lane width on roadway segments using generalized nonlinear models. Accident; Analysis and Prevention, 76, 83–91. doi:https://doi.org/10.1016/j.aap.2015.01.007
- Moghaddam, F. R., Afandizadeh, S., & Ziyadi, M. (2011). Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering, 9(1), 41–49.
- Saltelli, A. (2002). Making best use of model valuations to compute sensitivity indices. Computer Physics Communications, 145(2), 280–297. doi:https://doi.org/10.1016/S0010-4655(02)00280-1
- Saltelli, A. (2004). Global sensitivity analysis: An introduction. In Proceedings of the 4th International Conference on Sensitivity Analysis of Model Output (SAMO’04) (pp. 27–43).
- Saltelli, A., Chan, K., & Scott, E. M. (2009). Sensitivity analysis. Wiley.
- Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., & Tarantola, S. (2010). Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index. Computer Physics Communications, 181(2), 259–270. doi:https://doi.org/10.1016/j.cpc.2009.09.018
- Sameen, M., & Pradhan, B. (2017). Severity prediction of traffic accidents with recurrent neural networks. Applied Sciences, 7(6), 476. doi:https://doi.org/10.3390/app7060476
- Savolainen, P. T., Mannering, F. L., Lord, D., & Quddus, M. A. (2011). The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident; Analysis and Prevention, 43(5), 1666–1676. doi:https://doi.org/10.1016/j.aap.2011.03.025
- Sobol, I. M., & Kucherenko, S. S. (2005). Global sensitivity indices for nonlinear mathematical models Review. Wilmott, 2005(1), 56–61. doi:https://doi.org/10.1002/wilm.42820050114
- Valverde-Albacete, F. J., & Peláez-Moreno, C. (2014). 100% classification accuracy considered harmful: The normalized information transfer factor explains the accuracy paradox. PLoS One, 9(1), e84217. doi:https://doi.org/10.1371/journal.pone.0084217
- Wagner, H. M. (1995). Global sensitivity analysis. Operations Research, 43(6), 948–969.
- Wang, S., Minku, L. L., & Yao, X. (2014). Resampling-based ensemble methods for online class imbalance learning. IEEE Transactions on Knowledge and Data Engineering, 27(5), 1356–1368. doi:https://doi.org/10.1109/TKDE.2014.2345380
- Xie, Y., Zhang, Y., & Liang, F. (2009). Crash injury severity analysis using Bayesian ordered probit models. Journal of Transportation Engineering, 135(1), 18–25. doi:https://doi.org/10.1061/(ASCE)0733-947X(2009)135:1(18)
- Xie, Y., Zhao, K., & Huynh, N. (2012). Analysis of driver injury severity in rural single-vehicle crashes. Accident; Analysis and Prevention, 47, 36–44. doi:https://doi.org/10.1016/j.aap.2011.12.012
- Zeng, Q., & Huang, H. (2014). A stable and optimized neural network model for crash injury severity prediction. Accident; Analysis and Prevention, 73, 351–358. doi:https://doi.org/10.1016/j.aap.2014.09.006
- Zeng, Q., Huang, H., Pei, X., & Wong, S. C. (2016). Analytic methods in accident research modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks. Analytic Methods in Accident Research, 10, 12–25. doi:https://doi.org/10.1016/j.amar.2016.03.002
- Zong, F., Xu, H., & Zhang, H. (2013). Prediction for traffic accident severity: Comparing the Bayesian network and regression models. Mathematical Problems in Engineering, 2013. doi:https://doi.org/10.1155/2013/475194