Enhancing Road Accident Prediction: An Advanced Information Retrieval Approach with Enhanced Focal Loss

Abstract

Road accidents remain a significant global concern, causing substantial loss of life, injuries, and economic burden. This paper proposes an advanced information retrieval approach with enhanced focal loss to improve the accuracy of road accident prediction. The methodology incorporates an adaptive weighting mechanism, temporal information, spatial context, multiscale learning, and uncertainty-aware formulation to address the challenges posed by imbalanced datasets in traffic safety analyses. The enhanced focal loss function is compared with other commonly used loss functions, demonstrating superior performance in terms of accuracy, F1-score, precision, and recall, while maintaining reasonable specificity. The ability of the model to identify key risk factors and reduce false positives in high-risk areas has significant implications for road safety management, including resource allocation, infrastructure planning, public awareness, and policy development. This study also discusses the potential applicability of the enhanced focal loss function in various domains facing class imbalance issues, such as medical imaging, fraud detection, and cybersecurity. Future research directions include multimodal data fusion, transfer learning, explainable AI, real-time adaptation, and human-AI collaboration to further advance the field of road accident prediction using information retrieval and search technologies.