Identifying the crash characteristics on freeway segments based on different ramp influence areas

Abstract

Objective: This study aimed to explore the relationship between crash types and different freeway segments and identify the factors contributing to crashes on different freeway segments. Unlike most of the previous studies on freeway segments, this study separately investigates basic freeway segments, single ramp influence segments, and multiple ramp influence segments.

Methods: Nonlinear canonical correlation analysis (NLCCA) and proportionality test were used to identify the relationship between crash types and different freeway segments. The data sets for the different freeway segments accumulated for this study consist of 9,867 crash samples with complete information on all 22 chosen variables. A multinomial logit model (MNL) was used to estimate the influence of crash factors on different freeway segments.

Results: The results show that weaving and diverge overlap influence segments (WD) are more likely to have injury or fatal crashes; diverge and diverge overlap influence segments (DD) are more likely to have property damage–only (PDO) crashes; merge and merge overlap influence segments (MM) are more likely to have sideswipe crashes; and WD have non-sideswipe crashes; WD and weaving overlap influence segments (MW) are more likely to have rear end crashes; and MM segments are less likely to have hit object crashes. The contributing factors are identified by MNL and the results show that different traffic variables, environmental variables, vehicle variables, driver variables, and geometric variables significantly affected the likelihood of crashes on different freeway segments.

Conclusions: Investigation of crash types and factors contributing to crashes on different freeway segments is based on multiple ramp influence segments, which can promote a better understanding of the safety performance of various freeway segments.

Keywords:

• Freeway
• safety
• ramp influence segment
• multinomial logit model
• injury crash
• crash characteristic

Additional information

Funding

This research is supported by the Projects of International Cooperation and Exchange of the National Natural Science Foundation of China (51561135003), the Projects of the National Natural Science Foundation of China (71701046), the China Postdoctoral Science Foundation funded project (2017M571644l; 2018T110427), the Fundamental Research Funds for Central Universities (2242018R20003; YBJJ1533), and the Scientific Innovation Research of College Graduates in Jiangsu Province (KYLX_0173).