Architecture and Application of Traffic Safety Management Knowledge Graph Based on Neo4j

A large amount of traffic safety information has been generated. This will further promote the sustainable development of transport. However, its content, form, and structure are complex and scattered, lacking effective information integration and a comprehensive framework. Combined with the concept of safety analysis, a traffic safety management knowledge graph was designed for structured data, which include 54 types of node entities and 14 types of relationship entities. Six types of information were collected and imported, including illegal acts, vehicle failure, emergency response, legal norms, organization information, and road-related information. Ultimately, a knowledge query function was realized using Cypher, and an automatic Q & A function was created based on rule matching. A traffic accident knowledge graph was constructed for unstructured data, with people and institutions involved, vehicles involved, and accidents as the core, including 21 types of node entities and 22 types of relationship entities. Comparing the node entity extraction performance of Bert, Bert-CRF, Bert-BiLSTM, and Bert-BiLSTM-CRF models, Bert BiLSTM-CRF performs the best. The Bert model was used for relationship entity extraction. The traffic accident knowledge graph can structurally display accident information and support a query function to facilitate safety analysis.