BrightAccidentGraph: Accident Learning Attention Embeddings Based Multi-View Accident Knowledge Graph for Traffic Accident Reasoning

Traffic accident data analysis and reasoning are crucial for accident prevention and control. Constructing an accident knowledge graph from hybrid datasets of Chinese and English accidents is a valuable task. However, it is challenging due to the need to consider multiple perspectives and infer implicit relationships between actors and factors in complex traffic accidents. To address these challenges, this paper proposes an accident learning attention embeddings based multi-view accident knowledge graph for traffic accident reasoning named BrightAccidentGraph. First, this paper proposes a multi-source traffic accident dataset construction and preprocessing method for traffic accident judgement records published by the China Judgement Document Network and traffic accident records published by the UK’s Ministry of Transport. Then, traffic accident graph construction and portrait method is proposed, we demonstrate the efficiency of the proposed method by constructing several multi-view traffic accident portraits using a multi-source dataset. Furthermore, accident learning attention embeddings based multi-view accident knowledge graph construction and traffic accident reasoning method using deep learning are introduced. Experiments on two hybrid datasets verify the efficiency and merits of our method.