Revolutionizing Urban Traffic: A Study on Machine Learning Approaches for Real-Time Accident Detection and Traffic Congestion Prediction

Abstract

In the quest to enhance road safety, integrating machine learning techniques into road accident analysis offers substantial potential. This research utilizes visualization tools and predictive models to identify patterns and factors contributing to road accidents, aiming to provide actionable insights for prevention. We establish a framework to effectively predict and mitigate road accidents by leveraging data-driven strategies. Road accidents pose a significant public safety challenge, necessitating detailed analysis to comprehend their dynamics. The study includes various visualizations, such as monthly accident counts, correlations of accident factors, and time-based patterns, offering a granular view of the circumstances leading to accidents. The project involves data preprocessing, feature engineering, and applying machine learning algorithms to analyze and predict road accident severity. Data collection encompasses comprehensive datasets on road conditions, accident specifics, and environmental factors. Preprocessing cleanses data inconsistencies, while feature engineering extracts relevant attributes for model training. Models like Random Forest and Gradient Boosting classifiers are selected for their capacity to handle large datasets and their effectiveness in previous studies. Visualizations, including correlation heatmaps and bar charts of accidents by month and time of day, highlight critical insights. The analysis reveals seasonal and time-based trends, emphasizing the potential of machine learning in enhancing road safety by informing targeted interventions.