## 2 Related Works  
### Risk Factor Identification  
In current accident analysis methods, such as Accident Mapping Analysis (AcciMap) [8], the Human Factors Analysis and Classification System (HFACS) [9], and the Systems Theoretic Accident Model and Process (STAMP) [10], an important preliminary step is the identification of hazards, accident causes, risk factors, and other related entities. This typically requires a thorough manual review and analysis of accident investigation reports, followed by the extraction of relevant entities [11; 12], which is also referred to as an expert-based method. For example, [PERSON] et al. [13] used HFACS, with the help of research teams from universities and transportation companies, to categorize accident causes into five categories: unsafe behaviors, preconditions for unsafe behaviors, unsafe supervision, organizational influences, and outside factors. [PERSON] et al. [14] organized two human factor analysts, each with a Ph.D. and 30 years of experience, to categorize 37 accident investigation reports into 19 categories, identifying 1656 contributing and protective factors for road safety interventions. However, although this method can extract factors with relatively high accuracy, as the accident dataset continues to grow, it can lead to additional labor costs.  
In recent years, with the development of artificial intelligence (AI) and natural language processing (NLP) technology, text mining methods such as keyword extraction [15], topic modeling [16; 17], text classification [18; 19; 20; 21], and clustering analysis [22; 23; 24; 25] can automatically or semiautomatically obtain valuable information from accident investigation reports and are widely used in accident analysis. In keyword extraction, TF-IDF [26; 27] and text-rank [28] not only extract keywords but can also serve as prior knowledge for text classification. For example, [PERSON] et al. [29] used YAKE for keyword extraction and as a prior for LDA to identify accident causes, achieving 80% classification accuracy on aviation and pipeline accident datasets. [PERSON] et al. [30] combined convolutional neural networks (CNN) and LDA to classify 34 types of hazards in construction accidents, achieving an average F1 score of 71%. Building on this, [PERSON] et al. [31] used text-rank and graph convolutional networks (GCNs) to identify accident types and injury types, achieving an average F1 score of 74%. Although these methods can automate the analysis of accident reports, the accuracy of identification is difficult to guarantee.  
Named entity recognition (NER), one of the typical tasks in NLP, is a method for identifying entities with specific meanings or significance from text data [32]. In recent years, it has been widely used in various transportation fields such as aviation [33], maritime [34], road transport [35], and railways [36]. NER algorithms aim to identify the locations of entities in unstructured text and classify them into the correct categories [37]. For example, [PERSON] et al. [38] integrated bidirectional long short-term memory (BiLSTM), conditional random fields (CRF), and hidden Markov models (HMM) to identify 71 hazard sources in railway accidents, achieving an F1 score of 98.16%. However, they used 17 text augmentation algorithms to enhance the data. BERT has gained rapid popularity due to its rich word embeddings and low-cost continuous pretraining [39]. Its combination with the BiLSTM-CRF model has been shown to outperform other frameworks across multiple datasets [34; 36; 40]. However, unlike conventional NER tasks, the risk-factor-related entities we focused on were not general entities (e.g., names of people or places). Therefore, the BERT-BiLSTM-CRF framework needed not only to recognize but to understand the semantics of the entities themselves. This study enhances the semantic understanding capability of BERT by introducing domain-specific corpora for continuous pretraining, aiming to improve the accuracy of risk factor identification.