Assessing the Role of Intersection Proximity in Pedestrian Crashes: Insights from Data Mining Approach
Ahmed Hossain, Xiaoduan Sun, Subasish Das

TL;DR
This study analyzes pedestrian crashes near intersections using data mining to identify patterns based on distance, providing insights for targeted safety measures at non-intersection locations.
Contribution
It introduces a novel 'distance to intersection' framework and applies association rule mining to uncover complex crash patterns at non-intersection sites.
Findings
50% of crashes occur within 198 ft. of intersections.
Identified 60 top association rules across three zones.
Provided targeted countermeasures based on crash pattern insights.
Abstract
Although intersections are the most complex parts of the roadway network, pedestrian crashes at non-intersection locations are disproportionately frequent, highlighting a serious traffic safety concern. This study investigates non-intersection crashes involving pedestrians using a crash database (2017-2021) collected from Louisiana State. As the risk of pedestrian crashes tends to vary with distance from the intersection, the research team utilized a unique framework "distance to intersection" to capture the differences in crash patterns at non-intersection locations. The study identified that around 50% of non-intersection pedestrian crashes occurred within 198 ft. of the intersection. In the next step, the collected 3,135 pedestrian crashes at non-intersection locations during the study period were subdivided into three zones: D1 zone designates crashes occurring within 150 ft. of an…
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