Hybrid Deep Learning Models with Explainable AI and Reinforcement Learning for Traffic Accident Prediction

Traffic accidents remain a significant global concern, leading to fatalities, injuries, and substantial economic losses. Predicting traffic accidents with high accuracy is essential for improving road safety and reducing these adverse impacts. This research aims to explore the potential of hybrid deep learning models, integrating Explainable Artificial Intelligence (XAI) and Reinforcement Learning (RL), to enhance traffic accident prediction. The study focuses on evaluating the effectiveness of combining deep learning techniques, such as Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM), with XAI methods, including SHAP, Grad CAM, and LIME, for improving model interpretability. Furthermore, the inclusion of RL is analyzed for its ability to adapt predictive models to dynamic traffic conditions, enabling real time decision making. A systematic literature review (SLR) approach was employed to assess studies published between 2019 and 2025, providing insights into the integration of these technologies for traffic prediction. The findings reveal that hybrid models significantly outperform traditional approaches, improving prediction accuracy by up to 15% and enhancing model transparency by 2030%. These advancements offer practical implications for the development of intelligent transportation systems, particularly in realtime applications. The study concludes that the integration of deep learning, XAI, and RL provides a robust framework for more accurate, interpretable, and adaptable traffic accident prediction models, contributing to smarter traffic management and improved road safety.