A probabilistic reasoning approach to analyze the severity of single-vehicle crashes at mid-ramp locations

Abstract

Freeway ramps are one of the roadway elements that are considered as crash-prone sites with relatively more crashes per mile than other freeway segments. Among other crash types that occurred on freeway ramps, single-vehicle crashes have been found to be more severe. Thus, understanding the factors influencing the severity of single-vehicle crashes on freeway ramps is essential in improving the safety of our limited-access facilities. This study adopted a discrete Bayesian network (BN) approach to explore the probabilistic relationship among the potential factors associated with the severity of single-vehicle crashes at mid-ramp locations. The analysis was based on 6 041 single-vehicle crashes that occurred at the mid-ramp locations in California from 2009 to 2017. The findings indicated that ramp type, ramp traffic volume, road surface condition, and time of day were directly associated with the severity of single-vehicle crashes at the mid-ramp locations. The interdependency of off-ramps, ramp AADT of less than 13 000 vehicles per day, dry road surface condition, and off-peak hours were associated with the highest risk of fatal/severe injury crashes involving a single-vehicle. The study findings could potentially be used by transportation agencies in planning and implementing several strategies to improve the safety of freeway ramps.

Keywords

• Bayesian network (BN)
• crash severity
• Single-vehicle crash
• Mid-ramp location