TY - GEN
T1 - Prediction of fatality crashes with multilayer perceptron of crash record information system datasets
AU - Duong, Thanh Hung
AU - Qiao, Fengxiang
AU - Yeh, Jyh Haw
AU - Zhang, Yunpeng
N1 - Publisher Copyright:
©2020 IEEE
PY - 2020/9/26
Y1 - 2020/9/26
N2 - Despite the effort o f the authorities and researchers, there has been no sign o f decreasing in the num ber o f fatal crashes annually. To analyze the deadly collisions, researchers have focused on finding w hich factors affect injury severity, and thus m any crash prediction m odels for it had been developed. C om m only the injury severity is categorized into five different classes. Still, in m any studies, m inority classes like fatality and incapacitating injury w ere m erged so that the dataset becom es balanced, and the m odel can provide decent predictions. H ow ever, this approach does not help analyze the fatal crashes as they are joined w ith other types o f injury. Therefore, in this study, w e proposed a m ultilayer perceptron m odel for binary classification o f crash fatality. The m odel w as proved to be able to handle heavily im balanced datasets w hile providing decent perform ance. M oreover, a sensitivity analysis w as conducted on the input o f the m odel to estim ate the im portance o f crash-related factors.
AB - Despite the effort o f the authorities and researchers, there has been no sign o f decreasing in the num ber o f fatal crashes annually. To analyze the deadly collisions, researchers have focused on finding w hich factors affect injury severity, and thus m any crash prediction m odels for it had been developed. C om m only the injury severity is categorized into five different classes. Still, in m any studies, m inority classes like fatality and incapacitating injury w ere m erged so that the dataset becom es balanced, and the m odel can provide decent predictions. H ow ever, this approach does not help analyze the fatal crashes as they are joined w ith other types o f injury. Therefore, in this study, w e proposed a m ultilayer perceptron m odel for binary classification o f crash fatality. The m odel w as proved to be able to handle heavily im balanced datasets w hile providing decent perform ance. M oreover, a sensitivity analysis w as conducted on the input o f the m odel to estim ate the im portance o f crash-related factors.
KW - Artificial neural networks
KW - Im balanced dataset
KW - Injury severity
KW - M ultilayer perceptrons
KW - vehicle crash
UR - http://www.scopus.com/inward/record.url?scp=85112379684&partnerID=8YFLogxK
U2 - 10.1109/ICCICC50026.2020.09450248
DO - 10.1109/ICCICC50026.2020.09450248
M3 - Conference contribution
AN - SCOPUS:85112379684
T3 - Proceedings of 2020 IEEE 19th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020
SP - 225
EP - 229
BT - Proceedings of 2020 IEEE 19th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020
A2 - Wang, Yingxu
A2 - Ge, Ning
A2 - Lu, Jianhua
A2 - Tao, Xiaoming
A2 - Soda, Paolo
A2 - Howard, Newton
A2 - Widrow, Bernard
A2 - Feldman, Jerome
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2020
Y2 - 26 September 2020 through 28 September 2020
ER -