Forecasting Severity of Motorcycle Crashes Using Transfer Learning
Road traffic accident is a common type of disaster worldwide. Regardless of the road status, driver education or strict implementation of driving rules, accidents are bound to occur. Malaysia is no exception to this unfortunate disaster.
- Akar, Ö., & Güngör, O. (2013). Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation, 1(2).Google Scholar
- Bayer, J., Osendorfer, C., Korhammer, D., Chen, N., Urban, S., & van der Smagt, P. (2013). On fast dropout and its applicability to recurrent networks. arXiv preprint arXiv:1311.0701.
- Chollet, F. (2015). Keras.Google Scholar
- Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
- Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2015). 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).Google Scholar
- Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6645–6649). IEEE.Google Scholar
- Harnen, S., Umar, R. R., Wong, S. V., & Hashim, W. W. (2006). Motorcycle accident prediction model for junctions on urban roads in Malaysia. Advances in Transportation Studies, 8, 31–40.Google Scholar
- Hashmienejad, S. H. A., & Hasheminejad, S. M. H. (2017). Traffic accident severity prediction using a novel multi-objective genetic algorithm. International Journal of Crashworthiness, 1–16.Google Scholar
- Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
- Kingma, D., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- Liow. (2016, January 28). Retrieved April 07, 2017, from http://www.themalaymailonline.com.
- Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.
- Long, M., Wang, J., & Jordan, M. I. (2016). Deep transfer learning with joint adaptation networks. arXiv preprint arXiv:1605.06636.
- Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2015). Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865–873.Google Scholar
- Oord, A. V. D., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759.
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.Google Scholar
- Priyanka, A., & Sathiyakumari, K. (2014). A comparative study of classification algorithm using accident data. International Journal of Computer Science & Engineering Technology (IJCSET), 5(10), 1018–1023.Google Scholar
- Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.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.CrossRefGoogle 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.CrossRefGoogle Scholar
- Sapuan, M. S., Razali, A. M., & Zamzuri, Z. H. (2016). Modeling motorcycle road accidents with traffic offenses at several potential locations using negative binomial regression model in Malaysia. International Journal of Applied Mathematics and Statistics™, 54(3), 104–114.Google Scholar
- Sukor, N. S. A., Tarigan, A. K., & Fujii, S. (2016). Analysis of correlations between psychological factors and self-reported behavior of motorcyclists in Malaysia, depending on self-reported usage of different types of motorcycle facility. Transportation Research Part F: Traffic Psychology and Behaviour.Google Scholar
- Sutskever, I., Martens, J., & Hinton, G. E. (2011). Generating text with recurrent neural networks. In Proceedings of the 28th International Conference on Machine Learning (ICML-11) (pp. 1017–1024).Google Scholar
- Tieleman, T., & Hinton, G. (2012). Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4(2).Google Scholar