Abstract
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.
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Abdelwahab, H., & Abdel-Aty, M. (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, 6–13.
Akar, Ö., & Güngör, O. (2013). Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation, 1(2).
Alkheder, S., Taamneh, M., & Taamneh, S. (2017). Severity prediction of traffic accident using an artificial neural network. Journal of Forecasting, 36(1), 100–108.
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.
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
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.
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).
Duan, Y., Lv, Y., Liu, Y. L., & Wang, F. Y. (2016). An efficient realization of deep learning for traffic data imputation. Transportation Research Part C: Emerging Technologies, 72, 168–181.
El-Basyouny, K., & Sayed, T. (2006). Comparison of two negative binomial regression techniques in developing accident prediction models. Transportation Research Record: Journal of the Transportation Research Board, 1950, 9–16.
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.
Han, L., Luo, S., Yu, J., Pan, L., & Chen, S. (2015). Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes. IEEE Journal of Biomedical and Health Informatics, 19(2), 728–734.
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.
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.
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.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Hong, H., Pradhan, B., Jebur, M. N., Bui, D. T., Xu, C., & Akgun, A. (2016). Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environmental Earth Sciences, 75(1), 40.
Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sensing, 4(9), 2661–2693.
Kingma, D., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Lawrence, S., Giles, C. L., & Fong, S. (2000). Natural language grammatical inference with recurrent neural networks. IEEE Transactions on Knowledge and Data Engineering, 12(1), 126–140.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
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.
Manan, M. M. A., Jonsson, T., & Várhelyi, A. (2013). Development of a safety performance function for motorcycle accident fatalities on Malaysian primary roads. Safety Science, 60, 13–20.
Manan, M. M. A., & Várhelyi, A. (2012). Motorcycle fatalities in Malaysia. IATSS Research, 36(1), 30–39.
Olden, J. D., & Jackson, D. A. (2002). Illuminating the “black box”: A randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1), 135–150.
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.
Pei, X., Wong, S. C., & Sze, N. N. (2011). A joint-probability approach to crash prediction models. Accident Analysis and Prevention, 43(3), 1160–1166.
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.
Rahman, N. H., Rainis, R., Noor, S. H., & Mohamad, S. M. S. (2016). The Buffering analysis to identify common geographical factors within the vicinity of severe injury related to motor vehicle crash in Malaysia. World Journal of Emergency Medicine, 7(4), 278.
Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.
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.
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.
Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., et al. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298.
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.
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).
Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343.
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).
Wang, J., Zheng, Y., Li, X., Yu, C., Kodaka, K., & Li, K. (2015). Driving risk assessment using near-crash database through data mining of tree-based model. Accident Analysis and Prevention, 84, 54–64.
Zeng, Q., & Huang, H. (2014). A stable and optimized neural network model for crash injury severity prediction. Accident Analysis and Prevention, 73, 351–358.
Zur, R. M., Jiang, Y., Pesce, L. L., & Drukker, K. (2009). Noise injection for training artificial neural networks: A comparison with weight decay and early stopping. Medical Physics, 36(10), 4810–4818.
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Pradhan, B., Ibrahim Sameen, M. (2020). Forecasting Severity of Motorcycle Crashes Using Transfer Learning. 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_12
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DOI: https://doi.org/10.1007/978-3-030-10374-3_12
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