Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework-Reference-Cited by-同舟云学术

Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework

Published:2023-11-29 Issue:2 Volume:25 Page:194-201
ISSN:1538-9588
Container-title:Traffic Injury Prevention
language:en
Short-container-title:Traffic Injury Prevention

Author:

Wei Tianzheng¹^ORCID,Zhu Tong²,Lin Miao³,Liu Haoxue⁴

Affiliation:

1. School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan, China

2. College of Transportation Engineering, Chang’an University, Xi’an, China

3. China Automotive Technology and Research Center Co., Ltd., Tianjin, China

4. School of Automobile, Chang’an University, Xi’an, China

Funder

National Key R&D Program of China

Publisher

Informa UK Limited

Subject

Public Health, Environmental and Occupational Health,Safety Research

Link

https://www.tandfonline.com/doi/pdf/10.1080/15389588.2023.2284111

Reference31 articles.

1. Latent class analysis of factors that influence weekday and weekend single-vehicle crash severities

2. Obesity is associated with the future risk of heavy truck crashes among newly recruited commercial drivers

3. Comparison of moped, scooter and motorcycle crash risk and crash severity

4. Conditions for speeding behaviour: A comparison of car drivers and powered two wheeled riders

5. Motorcyclist accident involvement by age, gender, and risky behaviors in Taipei, Taiwan

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