Advertisement

Modeling Traffic Accident Severity Using Neural Networks and Support Vector Machines

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

Abstract

Recent studies have predicted that in 2030, traffic accidents will be the fifth leading cause of death worldwide. The costs of fatalities and driver injuries due to traffic accidents greatly affect the society. These insights call for investigating various aspects of traffic accident data analysis and modeling in numerous geographic regions (Sameen and Pradhan 2017a, b, c; Sameen et al. 2016). In particular, several researchers paid increasing attention to determining factors that greatly affect the severity of driver injuries caused by traffic accidents. Many approaches, such as logistic regression (LR) (Al-Ghamdi 2002), artificial neural networks (ANNs) (Delen et al. 2006; Moghaddam et al. 2011), support vector machines (SVMs) (Li et al. 2008, 2012), and Bayesian methods (Xie et al. 2009; de Oña et al. 2011), were explored to model traffic accident data.

References

  1. Al-Ghamdi, A. S. (2002). Using logistic regression to estimate the influence of accident factors on accident severity. Accident Analysis and Prevention, 34(6), 729–741.CrossRefGoogle Scholar
  2. Bui, D. T., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012). Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena, 96, 28–40.Google Scholar
  3. Bui, D. T., Bui, Q. T., Nguyen, Q. P., Pradhan, B., Nampak, H., & Trinh, P. T. (2017a). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and forest meteorology, 233, 32–44.Google Scholar
  4. Bui, D. T., Tuan, T. A., Hoang, N. D., Thanh, N. Q., Nguyen, D. B., Van Liem, N., & Pradhan, B. (2017b). Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides, 14(2), 447–458.Google Scholar
  5. Chen, C., Zhang, G., Qian, Z., Tarefder, R. A., & Tian, Z. (2016). Investigating driver injury severity patterns in rollover crashes using support vector machine models. Accident Analysis and Prevention, 90, 128–139.CrossRefGoogle Scholar
  6. Chen, C., Zhang, G., Tarefder, R., Ma, J., Wei, H., & Guan, H. (2015). A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. Accident Analysis and Prevention, 80, 76–88.CrossRefGoogle Scholar
  7. Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., ... & Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147–160.Google Scholar
  8. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273–297.Google Scholar
  9. Chong, M. M., Abraham, A., & Paprzycki, M. (2005). Traffic accident analysis using machine learning paradigms. Informatica (Slovenia), 29(1), 89–98.Google Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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
  12. Dong, N., Huang, H., & Zheng, L. (2015). Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects. Accident Analysis and Prevention, 82, 192–198.CrossRefGoogle Scholar
  13. Garrett, J. H. (1994). Where and why artificial neural networks are applicable in civil engineering.‏Google Scholar
  14. Jebur, M. N., Pradhan, B., & Tehrany, M. S. (2014). Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sensing of Environment, 152, 150–165.Google Scholar
  15. Jebur, M. N., Pradhan, B., & Tehrany, M. S. (2015). Manifestation of LiDAR-derived parameters in the spatial prediction of landslides using novel ensemble evidential belief functions and support vector machine models in GIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 674–690.Google Scholar
  16. Lee, J. H., Huang, Y., Dickman, M., & Jayawardena, A. W. (2003). Neural network modelling of coastal algal blooms. Ecological Modelling, 159(2), 179–201.CrossRefGoogle Scholar
  17. Li, X., Lord, D., Zhang, Y., & Xie, Y. (2008). Predicting motor vehicle crashes using support vector machine models. Accident Analysis and Prevention, 40(4), 1611–1618.CrossRefGoogle Scholar
  18. Li, Z., Liu, P., Wang, W., & Xu, C. (2012). Using support vector machine models for crash injury severity analysis. Accident Analysis and Prevention, 45, 478–486.CrossRefGoogle Scholar
  19. Moghaddam, F. R., Afandizadeh, S., & Ziyadi, M. (2011). Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering, 9(1), 41.Google Scholar
  20. Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences, 37(9), 1264–1276.Google Scholar
  21. Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747–759.Google Scholar
  22. Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350–365.Google Scholar
  23. Russo, B. J., Savolainen, P. T., Schneider, W. H., & Anastasopoulos, P. C. (2014). Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model. Analytic Methods in Accident Research, 2, 21–29.CrossRefGoogle Scholar
  24. 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
  25. 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
  26. 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
  27. 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
  28. Sharma, B., Katiyar, V. K., & Kumar, K. (2016). Traffic accident prediction model using support vector machines with Gaussian kernel. In Proceedings of Fifth International Conference on Soft Computing for Problem Solving (pp. 1–10). Springer Singapore.‏Google Scholar
  29. Xie, Y., Zhang, Y., & Liang, F. (2009). Crash injury severity analysis using Bayesian ordered probit models. Journal of Transportation Engineering, 135(1), 18–25.CrossRefGoogle Scholar
  30. Yu, R., & Abdel-Aty, M. (2013). Utilizing support vector machine in real-time crash risk evaluation. Accident Analysis and Prevention, 51, 252–259.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