Robotics & Machine Learning Daily News2024,Issue(Jun.28) :20-20.

Tongji University Researcher Provides Details of New Studies and Findings in the Area of Machine Learning (Commercial Truck Risk Assessment and Factor Analysis Based on Vehicle Trajectory and In-Vehicle Monitoring Data)

Robotics & Machine Learning Daily News2024,Issue(Jun.28) :20-20.

Tongji University Researcher Provides Details of New Studies and Findings in the Area of Machine Learning (Commercial Truck Risk Assessment and Factor Analysis Based on Vehicle Trajectory and In-Vehicle Monitoring Data)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from Shanghai, People’s Repub lic of China, by NewsRx journalists, research stated, “Truck crashes are general ly more serious than passenger vehicle crashes, and they cause more deaths per c rash worldwide per the U.S. Department of Transportation’s Fatality Analysis Rep orting System.” Our news editors obtained a quote from the research from Tongji University: “Ris k assessment and factor analysis are the keys to preventing truck crashes, but r esearch on commercial trucks has been limited. Currently, freight and insurance companies have collected extensive operating data, now making it possible to obt ain deep insights into truck crashes. Vehicle trajectory data and in-vehicle mon itoring data were collected for 596 large commercial trucks traveling in Shangha i, China, during 2019. A total of 22 variables were extracted, falling into thre e aspects: driving behavior, travel characteristics, and warning characteristics . The random forest algorithm was used to select the most important variables fo r further analysis. Four machine learning models and a mixed effects logistic re gression model were developed to link the high-importance variables with crash r isk.”

Key words

Tongji University/Shanghai/People’s Re public of China/Asia/Cyborgs/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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