ArticleInternational journal of environmental research and public health2020
Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Application of Filter Bank to Improve Fatigue Monitoring in Wearable EEG-Based Brain-Computer Interface.NeuroSci · 2026Article
- Assessment of obstructive sleep apnea rate and associated factors among Lebanese adults: a cross-sectional study.Frontiers in public health · 2025Article
- Analyzing the Prevalence of and Factors Associated with Road Traffic Crashes (RTCs) among Motorcyclists in Bangladesh.TheScientificWorldJournal · 2024Article
- An application of the hybrid AHP-PROMETHEE approach to evaluate the severity of the factors influencing road accidents.Heliyon · 2023Article
- Crash severity analysis and risk factors identification based on an alternate data source: a case study of developing country.Scientific reports · 2022Article
- Ride-hailing services: Competition or complement to public transport to reduce accident rates. The case of Madrid.Frontiers in psychology · 2022Article
- Convolutional Neural Network for Drowsiness Detection Using EEG Signals.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
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Abstract
Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.
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