Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis

• Published in: Journal of safety research Vol. 75; pp. 14 - 23
• Main Authors: Das, Subasish, Ashraf, Sruthi, Dutta, Anandi, Tran, Ly-Na
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2020; Elsevier Science Ltd
• Subjects: Accidents, Traffic - statistics & numerical data; Adolescent; Adult; Aged; Alcohol; Correspondence regression; Crash typing; Crashes; Dangerous Behavior; Drug Users - statistics & numerical data; Fatalities; Female; Humans; Impairment; Lighting; Louisiana; Male; Middle Aged; Mortality; Pattern recognition; Pedestrian crashes; Pedestrian impairment; Pedestrians; Pedestrians - statistics & numerical data; Regression Analysis; Residential location; Risk Factors; Roads; Traffic; Traffic accidents; Traffic accidents & safety; Young Adult; Louisiana; United States > US; Pedestrian impairment; Crash typing; Pattern recognition; Correspondence regression; Pedestrian crashes
• ISSN: 0022-4375; 1879-1247
• DOI: 10.1016/j.jsr.2020.07.001

•Pedestrian under influence (PUI) is a key safety concern.•Used correspondence regression analysis to identify the trends of key attributes.•Identified five major risk clusters. Introduction: Alcohol-related impairment is a key contributing factor in traffic crashes. However, only a few studies have focused on pedestrian impairment as a crash characteristic. In Louisiana, pedestrian fatalities have been increasing. From 2010 to 2016, the number of pedestrian fatalities increased by 62%. A total of 128 pedestrians were killed in traffic crashes in 2016, and 34.4% of those fatalities involved pedestrians under the influence (PUI) of drugs or alcohol. Furthermore, alcohol-PUI fatalities have increased by 120% from 2010 to 2016. There is a vital need to examine the key contributing attributes that are associated with a high number of PUI crashes. Method: In this study, the research team analyzed Louisiana’s traffic crash data from 2010 to 2016 by applying correspondence regression analysis to identify the key contributing attributes and association patterns based on PUI involved injury levels. Results: The findings identified five risk clusters: intersection crashes at business/industrial locations, mid-block crashes on undivided roadways at residential and business/residential locations, segment related crashes associated with a pedestrian standing in the road, open country crashes with no lighting at night, and pedestrian violation related crashes on divided roadways. The association maps identified several critical attributes that are more associated with fatal and severe PUI crashes. These attributes are dark to no lighting, open country roadways, and non-intersection locations. Practical Applications: The findings of this study may be used to help design effective mitigation strategies to reduce PUI crashes.