A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response. (February 2021)

• Record Type: Journal Article
• Authors: Zhang, Changjian; He, Jie; King, Mark; Liu, Ziyang; Chen, Yikai; Yan, Xintong; Xing, Lu; Zhang, Hao
• Is Part Of: Accident analysis and prevention. Volume 150(2021)
• Publication Date: 2021-02
• Subjects: Freeway safety -- Segments with horizontal curvature -- Vehicle kinetic response -- Negative binomial regression model -- Random effects negative binomial regression model; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105911
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: The remarkable relationship between the kinetic data and the crash risk was proved. Kinetic data is generated by the vehicle in real time while normal driving. A new method for high crash risk identification based on kinetic parameters was proposed. It can be effectively used to identify road design defects related to crash risk. Investigated the feasibility of kinetic parameters as crash surrogate events. Abstract: 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. 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. 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.