Application of knowledge graph and representation learning in the mining of road traffic accident data
The volume of data in traffic safety is substantial,containing rich semantic information.Extracting value from extensive road traffic accident data can provide crucial knowledge support for accident prevention and informed decision-making.However,conventional accident analysis methods face limitations when handling complex and multi-semantic intersecting accident data.Hence,this study proposes a method for mining accident data based on knowledge graph and knowledge representation learning.This method concatenates accident information by leveraging the graph structure and extracts associated information in spatial vectors,thereby enhancing the likelihood of discovering potential value in traffic accident data mining tasks.We establish a knowledge structure schema aligned with the general logic structure of road traffic accident occurrence and then proceed to construct a road traffic accident knowledge graph.In the mining process,we employ the TransE model for representation learning,wherein accident entities and causal relationships are mapped into vector space.Subsequently,semantic information between vectors is captured through vector matching operations to extract potential traffic accident information.In our initial experiments,we compare the model's performance on various datasets and vector dimensions,and we assess our method with real road accident data in different prediction tasks.The experimental results indicate that the proposed method exhibits high accuracy and robust semantic parsing capabilities.Additionally,utilizing vector spaces with appropriate dimensions enhances the accuracy of predictions for traffic-related data.This approach enables effective extraction of potential value from road traffic accident data.In fact,it helps mitigate biases caused by missing accident data in accident analysis and prevention decision-making processes,thereby enhancing the overall completeness of road traffic accident data.Therefore,the research results propose a novel method and idea for maximizing the utilization of fragmented information on road traffic accidents.
万方数据
维普
