Real-Time Risk Assessment of Hazmat Road Transportation Using Knowledge Graph Neural Recommendation Algorithm

Abstract:

Knowledge graph neural recommendation algorithm （KGCN） is used to assess hazmat road transportation risks in real time， aiming to reduce the probability of risks and avoid terrible accidents by detecting high-risk factors detected timely and adjusting the transportation status in advance based on real-time risk assessment report on hazmat road transportation. A knowledge graph describing hazmat road transportation accidents is constructed and embedded into a recommendation algorithm based on graph neural networks （GNN）， so that the probability of risk factors causing an accident is evaluated and a specialized risk assessment is given for each hazardous material road transport incident. The method simplifies real-time data processing operations and overcomes the difficulties of real-time data sparsity. Taking the cargo road transportation alarm data including 54 097 items as an example for calculation， the AUC value is calculated to be stable at around 0.83， indicating that the calculation results are reliable.