Multi-vehicle safety functions for freeway weaving segments using lane-level traffic data

Rim, Heesub; Abdel-Aty, Mohamed; Mahmoud, Nada

doi:10.1016/j.aap.2023.107113

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…On the other hand, the complicated hazards associated with every type of accident and how these dangers vary depending on the drivers’ demographics have not been examined to a considerable extent. Given the importance of creating more effective and targeted safety measures and interventions, which need a full awareness of the relationship between accident type and driver characteristics, this absence of information is considerable [ 22 , 23 ]. This is because the development of these measures and interventions is vital.…”

Section: Introductionmentioning

confidence: 99%

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Alshehri,

Alanazi,

Yosri

et al. 2024

PLoS ONE

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This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver’s age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

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Section: Introductionmentioning

confidence: 99%