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Fatal pedestrian crashes at intersections: Trend mining using association rules.

Subasish Das1, Reuben Tamakloe2, Hamsa Zubaidi3

  • 1Texas A&M Transportation Institute, 1111 RELLIS Parkway, Bryan, TX 77807, United States.

Accident; Analysis and Prevention
|July 25, 2021
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Summary
This summary is machine-generated.

Fatal pedestrian crashes are a serious concern, with many occurring at intersections. This study analyzed crash data to identify key factors, revealing that dark conditions without lighting significantly increase risks for pedestrians outside crosswalks.

Keywords:
Association rulesData miningFatal pedestrian crashIntersection crashesPedestrianPedestrian safety

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Area of Science:

  • Traffic Safety Research
  • Transportation Engineering
  • Data Mining in Accident Analysis

Background:

  • In 2018, 6,677 pedestrian fatalities occurred on US roadways.
  • Approximately 25% of these fatal pedestrian crashes occurred at or near intersections.
  • The high fatality rate necessitates a detailed examination of crash characteristics and scenarios.

Purpose of the Study:

  • To provide a comprehensive overview of characteristics and crash scenarios linked to fatal pedestrian incidents in the US.
  • To analyze five years (2014-2018) of fatal crash data, including detailed pedestrian crash typing.

Main Methods:

  • Applied association rules mining to four subgroups based on high-frequency fatal crash scenarios.
  • Utilized the 'a priori' algorithm with 'lift' as a performance measure to develop the top 20 rules for each subgroup.
  • Analyzed key variables including lighting conditions, vehicle movement, road type, and pedestrian demographics.

Main Results:

  • Identified frequent variable categories in fatal crashes: dark conditions with lighting, straight-moving vehicles, turning vehicles, local municipality streets, and pedestrians aged 45 and above.
  • Observed distinct rule patterns based on pedestrian location (within or outside crosswalks).
  • Found that dark conditions with no lighting are strongly associated with increased crashes for pedestrians outside crosswalks.

Conclusions:

  • The developed association rules provide quantitative measures of crash likelihood, enabling data-driven decision-making for safety improvements.
  • Findings can inform safety engineers and urban planners to enhance pedestrian safety, particularly at intersections.
  • Understanding specific crash scenarios, like those involving older pedestrians in dark, unlit areas, is crucial for targeted interventions.