Advertisement

Applications of Deep Learning in Severity Prediction of Traffic Accidents

  • Maher Ibrahim Sameen
  • Biswajeet PradhanEmail author
  • H. Z. M. Shafri
  • Hussain Bin Hamid
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 9)

Abstract

This study investigates the power of deep learning in predicting the severity of injuries when accidents occur due to traffic on Malaysian highways. Three network architectures based on a simple feedforward Neural Networks (NN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) were proposed and optimized through a grid search optimization to fine tune the hyperparameters of the models that can best predict the outputs with less computational costs. The results showed that among the tested algorithms, the RNN model with an average accuracy of 73.76% outperformed the NN model (68.79%) and the CNN (70.30%) model based on a 10-fold cross-validation approach. On the other hand, the sensitivity analysis indicated that the best optimization algorithm is “Nadam” in all the three network architectures. In addition, the best batch size for the NN and RNN was determined to be 4 and 8 for CNN. The dropout with keep probability of 0.2 and 0.5 was found critical for the CNN and RNN models, respectively. This research has shown that deep learning models such as CNN and RNN provide additional information inherent in the raw data such as temporal and spatial correlations that outperform the traditional NN model in terms of both accuracy and stability.

Keywords

Traffic accidents Recurrent neural networks CNN GIS LiDAR 

References

  1. 1.
    Sameen, M.I., Pradhan, B.: Assessment of the Effects of Expressway Geometric Design Features on the Frequency of Accident Crash Rates Using High-Resolution Laser Scanning Data and GIS, pp. 1–15. Geomatics, Natural Hazards and Risk (2016)Google Scholar
  2. 2.
    Lv, Y., Tang, S., Zhao, H.: Real-time highway traffic accident prediction based on the k-nearest neighbor method. In: 2009 IEEE International Conference on Measuring Technology and Mechatronics Automation ICMTMA’09, vol. 3, pp. 547–550‏ (2009)Google Scholar
  3. 3.
    Li, X., Lord, D., Zhang, Y., Xie, Y.: Predicting motor vehicle crashes using support vector machine models. Accid. Anal. Prev. 40(4), 1611–1618 (2008)CrossRefGoogle Scholar
  4. 4.
    Li, Z., Liu, P., Wang, W., Xu, C.: Using support vector machine models for crash injury severity analysis. Accid. Anal. Prev. 45, 478–486 (2012)CrossRefGoogle Scholar
  5. 5.
    Al-Ghamdi, A.S.: Using logistic regression to estimate the influence of accident factors on accident severity. Accid. Anal. Prev. 34(6), 729–741 (2002)CrossRefGoogle Scholar
  6. 6.
    Delen, D., Sharda, R., Bessonov, M.: Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accid. Anal. Prev. 38(3), 434–444 (2006)CrossRefGoogle Scholar
  7. 7.
    Moghaddam, F.R., Afandizadeh, S., Ziyadi, M.: Prediction of accident severity using artificial neural networks. Int. J. Civil Eng. 9(1), 41 (2011)Google Scholar
  8. 8.
    Ma, X., Yu, H., Wang, Y., Wang, Y.: Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE 10(3), e0119044 (2015)CrossRefGoogle Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105‏ (2012)Google Scholar
  10. 10.
    Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649‏ (2013)Google Scholar
  11. 11.
    Sarikaya, R., Hinton, G.E., Deoras, A.: Application of deep belief networks for natural language understanding. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP), 22(4), 778–784‏ (2014)CrossRefGoogle Scholar
  12. 12.
    Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32(14), 2627–2636 (1998)CrossRefGoogle Scholar
  13. 13.
    Mokhtarzade, M., Zoej, M.V.: Road detection from high-resolution satellite images using artificial neural networks. Int. J. Appl. Earth Obs. Geoinf. 9(1), 32–40 (2007)CrossRefGoogle Scholar
  14. 14.
    Baczyński, D., Parol, M.: Influence of artificial neural network structure on quality of short-term electric energy consumption forecast. IEE Proc-Gener. Transm. Distrib. 151(2), 241–245 (2004)CrossRefGoogle Scholar
  15. 15.
    Mia, M.M.A., Biswas, S.K., Urmi, M.C., Siddique, A.: An algorithm for training multilayer perceptron (MLP) for Image reconstruction using neural network without overfitting. Int. J. Sci. Techno. Res. 4(2), 271–275 (2015)Google Scholar
  16. 16.
    Yang, G.Y.C.: Geological Mapping from Multi-Source Data Using Neural Networks. University of Calgary, Geomatics Engineering (1995)Google Scholar
  17. 17.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  18. 18.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  19. 19.
    Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625–2634‏ (2015)Google Scholar
  20. 20.
    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S.: Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Maher Ibrahim Sameen
    • 1
  • Biswajeet Pradhan
    • 1
    • 2
    Email author
  • H. Z. M. Shafri
    • 1
  • Hussain Bin Hamid
    • 1
  1. 1.Department of Civil Engineering, Faculty of EngineeringGeospatial Information Science Research Center (GISRC), University Putra MalaysiaSerdangMalaysia
  2. 2.Faculty of Engineering and Information Technology, School of Systems, Management and LeadershipUniversity of Technology SydneyUltimoAustralia

Personalised recommendations