Risk Prediction of Urban Rail Transit Accidents: Application of LSTM-CRF Hybrid Model

As an important part of urban public transportation, urban rail transit has always been concerned about its safety and operational efficiency. However, due to the complexity and uncertainty of urban rail transit systems, how to effectively predict and reduce the risk of rail transit accidents has become an important topic of research. In recent years, deep learning techniques have achieved remarkable results in the field of risk prediction, especially the combined model of Long Short-Term Memory Networks (LSTM) and Conditional Random Fields (CRF) has demonstrated powerful performance in sequence labeling and prediction tasks. The aim of this study is to explore the application of LSTM-CRF hybrid models in urban rail transit accident risk prediction. In this paper, a dataset containing multiple accident features is constructed, and a hybrid LSTM-CRF-based model is designed, which is able to capture long-term dependencies in accident sequences and accurately annotate the accident risks through the CRF layer. In this paper, various optimization algorithms and regularization techniques are used to improve the generalization ability and prediction accuracy of the model. The results show that the hybrid LSTM-CRF model performs well in the accident risk prediction task, and is able to identify potentially high-risk time periods and regions more accurately than traditional methods. This study not only verifies the effectiveness of the LSTM-CRF hybrid model in urban rail transit accident risk prediction, but also provides new ideas and methods for risk prediction and safety management in related fields.