A meta fusion model combining geographic data and twitter sentiment analysis for predicting accident severity.

In recent years, advancements in deep learning and real-time data processing have significantly enhanced traffic management and accident prediction capabilities. Building on these developments, this study introduces an innovative approach ConvoseqNet to improve traffic accident prediction by integrating traditional traffic data with real-time social media insights, specifically using geographic data and Twitter sentiment analysis. ConvoseqNet combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks in a sequential architecture, enabling it to effectively capture complex spatiotemporal patterns in traffic data. To further enhance prediction accuracy, a meta-model called MetaFusionNetwork is proposed, which combines predictions from ConvoseqNet and a Random Forest Classifier. Results show that ConvoseqNet alone achieved the highest predictive accuracy, demonstrating its capacity to capture diverse accident-related patterns. Additionally, MetaFusionNetwork's performance highlights the advantages of combining model outputs for better prediction. This research contributes to real-time data-driven traffic management by leveraging innovative data fusion techniques, improving prediction accuracy, and providing insights into model interpretability and computational efficiency. By addressing the challenges of integrating heterogeneous data sources, this approach presents a significant advancement in traffic accident prediction and safety enhancement.