Investigating pedestrian crash patterns at high-speed intersection and road segments: Findings from the unsupervised learning algorithm

Abstract

Pedestrian crashes at high-speed locations are a persistent road safety concern. Driving at high speeds means that the driver has less time to react and make evasive maneuvers to avoid a pedestrian crash. On top of this, other crash-contributing factors such as humans (pedestrians or drivers), vehicles, roadways, and surrounding environmental factors actively interact together to cause a crash at high-speed locations. The pattern of pedestrian crashes also differs significantly according to the high-speed intersection and segment locations which require further investigation. This study applied association rules mining (ARM), an unsupervised learning algorithm, to reveal the hidden association of pedestrian crash risk factors according to the high-speed intersection and segments separately. The study used Louisiana pedestrian fatal and injury crash data (2010 to 2019). Any crash location with a posted speed limit of 45 mph or above is classified as a high-speed location. Based on the generated association rules, the results show that pedestrian crashes at a high-speed intersection are associated with the intersection geometry (3-leg) and control (1 stop, no traffic control device), driver characteristics (careless operation, failure to yield, inattentive-distracted, older, and younger driver), pedestrian-related factors (violations, alcohol/drug involvement), settings (open country, residential, business, industrial), dark lighting conditions and so on. Most pedestrian crashes at high-speed segments are associated with roadways with no physical separation, dark-no-streetlight conditions, open country locations, interstates and so on. The findings of the study may help to select appropriate countermeasures to reduce pedestrian crashes at high-speed locations.

Keywords

• High-speed
• Unsupervised learning
• Fatal
• Alcohol
• Dark-no-streetlight