Southeast University Reports Findings in Accident Research (Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models)

2021 MAR 11 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Accident Research is the subject of a report. According to news reporting originating from Nanjing, People’s Republic of China, by NewsRx correspondents, research stated, “Single-vehicle crashes are more fatality-concentrated and have posed increasing challenges in traffic safety, which is of great research necessity. Tremendous previous studies have conducted relevant analysis with econometric modeling approaches, whereas the ability of non-parametric methods to predict crash severity is still smattering of knowledge.”

Our news editors obtained a quote from the research from Southeast University, “Consequently, the main objective of this paper is to conduct single-vehicle crash severity prediction with different tree-based and non-parameter models. An alternate aim is to identify the intrinsic mechanism of how contributing factors determine single-vehicle crash severity. By virtue of Grid-Search method, this paper conducted fine-tuning of different models to obtain the best performances based on five crash severity sub-datasets. For model evaluation, the accuracy indicators were calculated in training, validation and test sets, respectively. Besides, feature importance extraction was undertaken based on the results of model comparison. The finding indicated that these models didn’t exhibit a huge performance difference for crash severity prediction in the same severity level; however, the performances of the models did vary among different datasets, with an average training accuracy of 99.27 %, 96.4 %, 86.98 %, 86.84 %, 71.76 % in fatal injury, severe injury, visible injury, complaint of pain, PDO crash datasets, respectively. Additionally, it was found that in each severity dataset, the indicator urban freeways is a determinant factor that leads to the occurrence of crashes while rural freeways is more related to more severe crashes (i.e., fatal and severe crashes). This paper can provide valuable information for model selection and tuning in accident severity prediction.”

According to the news editors, the research concluded: “Future research could consider the influences that temporal instability of contributing features has on the model performances.”

This research has been peer-reviewed.

For more information on this research see: Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models. Accident Analysis & Prevention, 2021;153:106034. Accident Analysis & Prevention can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Accident Analysis & Prevention - http://www.journals.elsevier.com/accident-analysis-and-prevention/)

The news editors report that additional information may be obtained by contacting Xintong Yan, School of Transportation, Southeast University, Si pai lou, Nanjing, 210096, People’s Republic of China. Additional authors for this research include Jie He, Changjian Zhang, Ziyang Liu, Boshuai Qiao and Hao Zhang.

Publisher contact information for the journal Accident Analysis & Prevention is: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

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