| dc.description.abstract | Road accidents have recently emerged as a
significant threat, ranking as the ninth leading cause of
fatalities globally. The high cost of traffic-related deaths
and property damage is particularly burdensome in
developing countries. Investigating the factors
contributing to accidents and accurately predicting
accident severity are crucial steps in mitigating future
incidents. Traditional methods for predicting road
accident severity have relied on shallow models and
statistical approaches, with limited exploration of deep
learning techniques. This study conducts a comparative
analysis of LSTM, CatBoost, and CNN models for
predicting road accident severity, aiming to identify the
model that most accurately forecasts accident severity
based on road accident data. To address the challenges
of small datasets, limited coverage, and real-time
applicability, we applied these models to both Balanced
and Unbalanced US Accident datasets. Additionally,
three feature selection algorithms Random Forest,
Decision Tree, and CatBoost were employed to extract
the most relevant features from the datasets. Our results
demonstrate that the combined approach of CatBoost
feature selection and LSTM modeling outperforms
standalone models, achieving an accuracy of 98.57%. | en_US |
