Accident Blackspots Detection and Visualization using Machine Learning

Abstract

Road traffic accidents are a major public safety concern worldwide, leading to significant loss
of life, injuries, and economic damage. Identifying accident-prone areas, commonly known
as blackspots, is essential for implementing effective preventive measures. This project
presents an intelligent system for Accident Blackspots Detection and Visualization using
Machine Learning, aimed at analyzing historical accident data to identify high-risk zones and
support decision-making for traffic authorities.
The proposed system utilizes machine learning algorithms to process large-scale datasets
containing accident records, including parameters such as location, time, weather conditions,
road type, and vehicle details. Techniques such as clustering (e.g., K-Means) and
classification models are employed to detect patterns and group accident-prone regions based
on severity and frequency. Feature engineering and data preprocessing methods are applied to
improve prediction accuracy and reliability.