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The pattern of pedestrian crashes varies greatly depending on lightingcircumstances, emphasizing the need of examining pedestrian crashes in variouslighting conditions. Using Louisiana pedestrian fatal and injury crash data(2010-2019), this study applied Association Rules Mining (ARM) to identify thehidden pattern of crash risk factors according to three different lightingconditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based onthe generated rules, the results show that daylight pedestrian crashes areassociated with children (less than 15 years), senior pedestrians (greater than64 years), older drivers (>64 years), and other driving behaviors such asfailure to yield, inattentive/distracted, illness/fatigue/asleep. Additionally,young drivers (15-24 years) are involved in severe pedestrian crashes indaylight conditions. This study also found pedestrian alcohol/drug involvementas the most frequent item in the dark-with-streetlight condition. This crashtype is particularly associated with pedestrian action (crossingintersection/midblock), driver age (55-64 years), speed limit (30-35 mph), andspecific area type (business with mixed residential area). Fatal pedestriancrashes are found to be associated with roadways with high-speed limits (>50mph) during the dark without streetlight condition. Some other risk factorslinked with high-speed limit related crashes are pedestrians walkingwith/against the traffic, presence of pedestrian dark clothing, pedestrianalcohol/drug involvement. The research findings are expected to provide animproved understanding of the underlying relationships between pedestrian crashrisk factors and specific lighting conditions. Highway safety experts canutilize these findings to conduct a decision-making process for selectingeffective countermeasures to reduce pedestrian crashes strategically.

Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions