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Summary & Details

Construction of Injury Prediction Model for Car Occupants using Gradient-Boosting Decision Tree Model

勾配ブースティング決定木を用いた乗員傷害予測モデルの構築

Delivery: Available on this site

Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20225267

Paper/Info type: Proceedings (Spring)

No.60-22

Pages: 1-6(Total 6 p)

Date of publication: May 2022

Publisher: JSAE

Language: Japanese

Event: 2022 JSAE Annual Congress (Spring)

Detailed Information

Author(J)	1) 高橋啓太, 2) 宮崎祐介, 3) 北村光司, 4) 佐藤房子
Author(E)	1) Keita Takahashi, 2) Yusuke Miyazaki, 3) Koji Kitamura, 4) Fusako Sato
Affiliation(J)	1) 東京工業大学, 2) 東京工業大学, 3) 産業技術総合研究所, 4) 日本自動車研究所
Affiliation(E)	1) Tokyo Institute of Technology, 2) Tokyo Institute of Technology, 3) AIST, 4) JARI
Abstract(J)	本研究では，交通事故における乗員の死亡・重傷リスクを予測する機械学習モデルを構築した．予測モデルとして従来のロジスティック回帰に代わりLightGBMを採用し，また予測に用いる特徴量とデータ表現を検討することで，予測精度が向上することを確認した． Translation
Abstract(E)	The purpose of this study was to develop a machine learning model for predicting serious injury risk of occupants. LightGBM was used to construct a severe injury prediction model for the NASS-CDS database in US. Furthermore, by examining the features and data representation of the prediction model, the prediction accuracy was improved compared to the conventional logistic regression model.

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