ArticlePloS one2021
Crash severity analysis of vulnerable road users using machine learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.Scientific reports · 2026Article
- A cyclist-centric 360Scientific data · 2026Article
- Safety-oriented and explainable machine learning for KSI crash risk prediction: Evidence from the United Kingdom.PloS one · 2026Article
- OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.Scientific reports · 2025Article
- Article
- Using Machine Learning on V2X Communications Data for VRU Collision Prediction.Sensors (Basel, Switzerland) · 2023Article
- Evaluating the effect of drunk driving on fatal injuries among vulnerable road users in Taiwan: a population-based study.BMC public health · 2022Article
- Development and Initial Validation of the Safety Training Engagement Scale (STE-S).European journal of investigation in health, psychology and education · 2022Article
- Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users-pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups-for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.
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