Deep Learning Models for Traffic Accident Prediction

Road traffic accidents are a major global safety concern, resulting in significant loss of life and economic damage each year. Traditional accident prediction approaches rely on statistical and rule-based models, which struggle to capture complex non-linear relationships among traffic, environmental, and human behavioural factors. Recent advancements in deep learning offer powerful tools for modelling such complex patterns using large-scale traffic data. This research paper explores deep learning-based approaches for traffic accident prediction, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and hybrid models. The paper discusses data sources, feature extraction, model architectures, performance evaluation metrics, challenges, and future research directions. Experimental results reported in existing studies demonstrate that deep learning models significantly outperform traditional machine learning techniques in predicting traffic accidents, enabling proactive road safety management.