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Crash severity analysis of vulnerable road users using machine learning.

Md Mostafizur Rahman Komol1,2, Md Mahmudul Hasan1,2, Mohammed Elhenawy1,2

  • 1Centre for Accident Research and Road Safety-Queensland, Queensland University of Technology, Brisbane, Australia.

Plos One
|August 5, 2021
PubMed
Summary
This summary is machine-generated.

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Machine learning models analyzed road crash severity for vulnerable road users (VRU). Random Forest models identified critical factors, with motorcyclists facing the highest risk.

Area of Science:

  • Transportation Safety
  • Machine Learning Applications
  • Public Health

Background:

  • Road crashes pose a universal threat, with vulnerable road users (VRU) like pedestrians, bicyclists, and motorcyclists experiencing disproportionately high crash severity.
  • Understanding the factors contributing to VRU injury severity is crucial for developing effective safety interventions.

Purpose of the Study:

  • To employ machine learning classification models to predict injury severity for pedestrians, bicyclists, and motorcyclists.
  • To identify and analyze critical road crash features influencing severity across different VRU groups and for all VRU combined.
  • To compare the performance of different machine learning algorithms in modeling VRU crash severity.

Main Methods:

  • Utilized crash data from Queensland, Australia (2013-2019) for pedestrian, bicyclist, and motorcyclist incidents.

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  • Applied supervised machine learning algorithms: K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Random Forest (RF).
  • Input features included 17 road crash parameters covering user characteristics, environment, vehicle, driver, time, road, traffic, and speed.

    • Main Results:

    • Random Forest models demonstrated robust performance across VRU categories (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%).
    • Feature importance analysis revealed key factors impacting crash severity for each VRU group.
    • Partial dependency plots illustrated the relationship between crash parameters and severity levels for different VRU.

    Conclusions:

    • Motorcyclists are most vulnerable to higher crash severity, followed by pedestrians and bicyclists.
    • Machine learning, particularly Random Forest, is effective in modeling VRU injury severity and identifying critical risk factors.
    • Findings provide insights for targeted safety strategies to reduce VRU fatalities and injuries.