Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions
Ahmed Hossain, Xiaoduan Sun, Raju Thapa, Julius Codjoe

TL;DR
This study uses Association Rules Mining on Louisiana crash data to uncover how lighting conditions influence pedestrian crash risk factors, aiding targeted safety interventions.
Contribution
It applies ARM to identify lighting-specific crash patterns and risk factors, offering new insights for pedestrian safety strategies under different lighting conditions.
Findings
Daylight crashes linked to children, seniors, and distracted driving.
Dark-with-streetlight crashes involve alcohol and specific locations.
High-speed roads without streetlights are associated with fatal crashes.
Abstract
The pattern of pedestrian crashes varies greatly depending on lighting circumstances, emphasizing the need of examining pedestrian crashes in various lighting conditions. Using Louisiana pedestrian fatal and injury crash data (2010-2019), this study applied Association Rules Mining (ARM) to identify the hidden pattern of crash risk factors according to three different lighting conditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based on the generated rules, the results show that daylight pedestrian crashes are associated with children (less than 15 years), senior pedestrians (greater than 64 years), older drivers (>64 years), and other driving behaviors such as failure to yield, inattentive/distracted, illness/fatigue/asleep. Additionally, young drivers (15-24 years) are involved in severe pedestrian crashes in daylight conditions. This study also found pedestrian…
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
