Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis |
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| Institution: | 1. Texas A&M Transportation Institute, 1111 RELLIS Parkway, Bryan, TX 77807, United States;2. Texas A&M Transportation Institute, 3135 TAMU, College Station, TX 77843, United States;3. Department of Computer Science, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249-0667, United States;4. Biomedical Sciences, Texas A&M University, 660 Raymond Stotzer Pkwy, College Station, TX 77843, United States;1. Ontario Ministry of Transportation, Safety Program Development Branch, Research and Evaluation Office, Toronto, Canada;2. The University of New South Wales, School of Population Health, Sydney, Australia;3. The George Institute for Global Health, Sydney, Australia;4. Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada;1. School of Labor and Employment Relations, University of Illinois at Urbana-Champaign, 504 E. Armory Ave, 247E LER Building, Champaign, IL 61820, United States;2. Department of Psychology, North Carolina State University, 640 Poe Hall, 2310 Katharine Stinson Dr., Raleigh, NC 27695-7650, United States;3. Department of Management and International Business, College of Business, Florida International University, Modesto A. Maidique Campus, 11200 S.W. 8th St, MANGO 472, Miami, FL 33199, United States;1. Civil and Environmental Engineering, California Polytechnic State University, United States;2. South Carolina Department of Transportation, Columbia, SC, United States;3. Wyoming Technology Transfer Center, Department of Civil & Architectural Engineering, University of Wyoming, 1000 E. University Avenue, Dept. 3295 Laramie, WY 82071, United States;1. Department of Agribusiness and Applied Economics, North Dakota State University, United States;2. Department of Applied Economics, University of Minnesota, United States |
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| Abstract: | 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. |
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| Keywords: | Pedestrian crashes Pedestrian impairment Pattern recognition Correspondence regression Crash typing |
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