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Traffic Injury Prevention Volume 23, 2022 - Issue sup1: Peer-Reviewed Journal for the 66th Annual Scientific Conference of the Association for the Advancement of Automotive Medicine (AAAM)
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Short Communications from the AAAM 66th Annual Conference

Construction of collision-type prediction model based on light gradient boosting machine algorithm and elucidation of data causality and relevance by Bayesian network

Junhao Weia Tokyo Institute of Technology, Meguro, Tokyo, JapanView further author information
,
Yusuke Miyazakia Tokyo Institute of Technology, Meguro, Tokyo, JapanCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-7863-7704View further author information
ORCID Icon,
Koji Kitamurab National Institute of Advanced Industrial Science and Technology, Koto, Tokyo, JapanView further author information
&
Fusako Satoc Japan Automobile Research Institute, Tsukuba, Ibaraki, JapanORCID Iconhttps://orcid.org/0000-0001-6517-8924View further author information
ORCID Icon
Pages S181-S182 | Published online: 01 Nov 2022

References

  • Malliaris AC, Digges KH, DeBlois JH. 1997 Feb 24–27. Relationships between crash casualties and crash attributes. In: SAE International Congress & Exposition Paper, Detroit, MI, p. 970393.
  • Meng Q. 2017. LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 30:2.
  • National Highway Traffic Safety Administration. n.d. Crashworthiness data system | NHTSA. https://www.nhtsa.gov/data/crash-data-systems.

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