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<div><p>To improve traffic safety in mixed traffic involving human-driven and autonomous vehicles, this study explored safety risk factors from multiple perspectives. Based on crash reports involving autonomous vehicles (AVs) in the California, United States, the XGBoost algorithm and Sh...

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Bibliographic Details
Main Author:	Yaqin He (4627711) (author)
Other Authors:	Jun Xia (135528) (author), Jiayin Dai (218672) (author)
Published:	2025
Subjects:	Biotechnology Ecology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified shapley additive explanations emergency braking events driverless taxi operations association rule mining safety improvement suggestions risk perception factors risk factors associated different traffic participants improve traffic safety using data collected safety risks mixed traffic factors contributing field data examined using xlink "> xgboost algorithm united states road sections multiple perspectives multiple lanes mixed driving central medians average degree autonomous vehicles automatic vehicles aggressiveness method adverse environments
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