Quantification of Rear-End Crash Risk and Analysis of Its Influencing Factors Based on a New Surrogate Safety Measure

Table 1

Summary of studies on driving risk-related factors.

Author	Research purpose	Experimental methods	Sample size (number of participants)	Analytical methods	Main findings
Hayley et al. [23]	To investigate the influence of driver’s personality characteristics on risky driving behavior.	Online survey	175 drivers (79 female) aged 18–64 years old.	Regression analyses	Risky driving has a greater correlation with emotion recognition and expression levels, and less correlation with age.
Harbeck et al. [24]	To examine risky driving in relation to psychological variables.	Online survey	601 participants (230 female) aged 17–25 years old.	Statistical analyses	The results of the proposed model show that young driver’s risky driving is related to risk perception, response cost, and rewards.
Ulleberg and Rundmo [25]	To understand the underlying mechanism of risky driving behavior through the combination of personality traits and social cognitive methods.	Questionnaire	3942 participants (2208 female) aged 16–23 years old.	Structural equation model (SEM)	The personality of a driver indirectly affects risky driving behaviors mainly by influencing behavioral attitudes.
Tao et al. [2]	To investigate the relationship between gender, age, self-reported risky driving behaviors, and crash risk.	Questionnaire	511 participants (195 female) aged 36–45 years old.	Structural equation model (SEM)	The results show that both driving experience and dangerous driving behavior affect the risk of accidents. The driver’s gender has little influence on dangerous driving behavior and accident risk.
Teye-Kwadjo [26]	To investigate the impact of driver’s risk perception on risky driving behavior.	Questionnaire	519 participants (127 female) aged 20–59 years old.	Structural equation model (SEM)	The research results show that risk perception has an impact on drivers’ risky driving behaviors. Male and female drivers and married and unmarried drivers have different preferences for risky driving behaviors.
Shangguan et al. [27]	To evaluate the rear-end crash risks under adverse environment.	Driving simulation	32 participants (12 female) aged 23–45 years old.	Survival analysis	The results show that the lower visibility leads to higher rear-end crash risk, and road alignment has a significant impact on crash risk.
Precht et al. [28]	To identify the main influencing factors contributing to driving risks.	Naturalistic driving data	108 trip segments.	Generalized linear mixed models (GLMMs)	Driving violations are related to anger, the presence of passengers, and personal differences. In addition, secondary tasks that cause distraction of the driver’s visual attention and complex driving tasks are associated with high driving risks.
Pnina et al. [14]	To investigate the interactions between driving context and their associations with risky driving behaviors of young novice drivers.	Naturalistic driving data	81 teenager drivers (43 female), average age 16.48 years old (SD = 0.33).	Passion regression analyses	Driving own has a higher high-risk driving than shared vehicle, and driving during the day has a higher risky driving rate than driving at night.
Chen et al. [29]	To explore the contributing factors to crash risk during lane-changing process.	Naturalistic driving data	579 lane-changing vehicle groups	Mixed regression models	The distance between the lane-changing vehicle and the preceding vehicle in the lane before the lane-changing significantly affects lane-changing safety.