@article{ref1,
title="Interpretable machine learning for evaluating risk factors of freeway crash severity",
journal="International journal of injury control and safety promotion",
year="2024",
author="Samerei, Seyed Alireza and Aghabayk, Kayvan",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is crucial for comprehending ML results and aiding informed decision-making. This study aims to implement an interpretable ML to visualize the impacts of factors on crash severity using 5 years of freeways data from Iran. <br><br>METHODS including classification and regression trees (CART), K-nearest neighbours (KNNs), random forest (RF), artificial neural network (ANN) and support vector machines (SVM) were applied, with RF demonstrating superior accuracy, recall, F1-score and ROC. The accumulated local effects (ALE) were utilized for interpretation. <br><br>FINDINGS suggest that light traffic conditions (volume/capacity < 0.5) with critical values around 0.05 or 0.38, and higher proportion of large trucks and buses, particularly at 10% and 4%, are associated with severe crashes. Additionally, speeds exceeding 90 km/h, drivers younger than 30 years, rollover crashes, collisions with fixed objects and barriers, nighttime driving and driver fatigue elevate the likelihood of severe crashes.<p /> <p>Language: en</p>",
language="en",
issn="1745-7300",
doi="10.1080/17457300.2024.2351972",
url="http://dx.doi.org/10.1080/17457300.2024.2351972"
}