Abstract:
Dhaka, known for its status as one of the world's most densely populated areas, grapples
with numerous challenges associated with urbanization and safety. This study focuses on
identifying accident-prone zones within the Dhaka Metropolitan City by analyzing data
collected from 2019 to 2023. Ten specific locations were investigated using various
machine learning models: Decision Tree Classifier, Gradient Boosting Classifier, Random
Forest, Gaussian Naive Bayes, and Logistic Regression. Evaluation metrics, including
precision, recall, accuracy, F1-score, and standard deviation, were utilized, with the
Random Forest classifier exhibiting the highest accuracy at 94.39%. The analysis
encompassed both monthly and annual accident data, revealing trends and probabilities
associated with different incident types. The study's implications suggest potential
advancements in accident investigation by incorporating advanced machine learning
techniques, integrating diverse datasets, and expanding the geographical coverage to
encompass the entire metropolitan area. This comprehensive approach aims to bolster
accident prevention measures and enhance urban safety. Overall, this research contributes
to a deeper understanding of accident-prone regions and proposes strategies for targeted
interventions to improve safety within metropolitan areas
