Despite the effort of the authorities and researchers, there has been no sign of decreasing in the number of fatal crashes annually. To analyze the deadly collisions, researchers have focused on finding which factors affect injury severity, and thus many crash prediction models for it had been developed. Commonly the injury severity is categorized into five different classes. Still, in many studies, minority classes like fatality and incapacitating injury were merged so that the dataset becomes balanced, and the model can provide decent predictions. However, this approach does not help analyze the fatal crashes as they are joined with other types of injury. Therefore, in this study, we proposed a multilayer perceptron model for binary classification of crash fatality. The model was proved to be able to handle heavily imbalanced datasets while providing decent performance. Moreover, a sensitivity analysis was conducted on the input of the model to estimate the importance of crash-related factors.
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Duong, Thanh Hung; Qiao, Fengxiang; Yeh, Jyh-Haw; and Zhang, Yunpeng. (2020). "Prediction of Fatality Crashes with Multilayer Perceptron of Crash Record Information System Datasets". 2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), 225-229. https://doi.org/10.1109/ICCICC50026.2020.9450248