Research Results from Southeast University Update Knowledge of Risk Management (A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response)

2020 DEC 23 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A new study on Risk Management is now available. According to news reporting originating in Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “With the development and maturation of vehicle-based data acquisition technology, in-vehicle data is increasingly being used to explore road safety. This paper reports on research that analyzed the real-time tire force data (kinetic response) obtained from vehicle kinetic experiments, and constructed a new approach for identifying the high-risk of crashes on freeway segments with horizontal curvature.”

Funders for this research include National Natural Science Foundation of China, Henan science and technology project, Jiangsu Creative PHD student sponsored project.

The news reporters obtained a quote from the research from Southeast University, “First, the road was divided into 1km units. Then, taking into account the characteristics of freeway alignment, each segment with horizontal curve was selected as the object of subsequent analysis. Automotive instrumentation was used to obtain a measure of tire force in the course of normal driving. The entire data set was preprocessed according to rate of change and the density of the data was reduced. By defining the outliers of the kinetic data and conducting factor analysis, two representative crash risk indicators of longitudinal and lateral stability were obtained. Negative binomial regression model (NBR model) and random effects negative binomial regression model (RENBR model) were constructed and jointly applied based on the new indicators to predict the risk value of horizontal curve segments. The method showed good prediction performance (71.8 %) for high-risk road segments with design flaws, but the predicted effect for low-risk road segments was not ideal.”

According to the news reporters, the research concluded: “This study not only illustrated the effectiveness of in-vehicle data in assessing road crash risk by coupling multiple kinetic parameters, but also provided support for freeway safety research using surrogate measures of risk when there is a lack of crash statistics.”

This research has been peer-reviewed.

For more information on this research see: A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response. Accident Analysis & Prevention, 2020;150:105911. 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/)

Our news correspondents report that additional information may be obtained by contacting Changjian Zhang, School of Transportation, Southeast University, Nanjing, 210018, People’s Republic of China. Additional authors for this research include Jie He, Mark King, Ziyang Liu, Yikai Chen, Xintong Yan, Lu Xing and Hao Zhang.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2020.105911. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

The publisher of the journal Accident Analysis & Prevention can be contacted at: Pergamon-Elsevier Science LTD, the Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

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