Exploring the Factors Influencing Traffic Accidents: An Analysis of Black Spots and Decision Tree for Injury Severity

Abdullah, Pires; Sipos, Tibor

doi:10.3311/pptr.22392

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…This approach aids in understanding the contributing factors and potentially mitigating their impact [49]. The spatial distribution of road traffic accidents and the identification of factors associated with these accidents using a decision tree classification approach was performed in [50]. This machine learning method was instrumental in identifying accident hot spots during peak and off-peak hours.…”

Section: Machine Learning In Black Spot Identificationmentioning

confidence: 99%

Leveraging Positive-Unlabeled Learning for Enhanced Black Spot Accident Identification on Greek Road Networks

Sevetlidis,

Pavlidis,

Mouroutsos

et al. 2024

Computers

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Identifying accidents in road black spots is crucial for improving road safety. Traditional methodologies, although insightful, often struggle with the complexities of imbalanced datasets, while machine learning (ML) techniques have shown promise, our previous work revealed that supervised learning (SL) methods face challenges in effectively distinguishing accidents that occur in black spots from those that do not. This paper introduces a novel approach that leverages positive-unlabeled (PU) learning, a technique we previously applied successfully in the domain of defect detection. The results of this work demonstrate a statistically significant improvement in key performance metrics, including accuracy, precision, recall, F1-score, and AUC, compared to SL methods. This study thus establishes PU learning as a more effective and robust approach for accident classification in black spots, particularly in scenarios with highly imbalanced datasets.

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Section: Machine Learning In Black Spot Identificationmentioning

confidence: 99%