BN-Bow-Tie model-based real-time risk assessment mechanism of dangerous goods transport

ObjectiveDangerous goods road transport accidents could easily lead to mass casualties. This study identifies key contributing factors based on historical accident lessons, and establishes a real-time risk assessment mechanism to enhance safety and reduce accident probability.MethodFirst, data of 736 historical accidents were collected. Then, BN-Bow-Tie model was constructed by combining Bow-Tie graph with Bayesian networks. Finally, the coupling relations among factors from five dimensions were analyzed, i.e., drivers, vehicles, roads, cargo, and environment.Accident types, consequences, and casualty situations were treated as events.ResultThrough Bayesian network parameter learning, it was found that improper driving operations, tank transport, vehicle equipment failures, flammable liquids materials, and driving between 0:00 and 6:00 were major contributing factors.The intelligent enforcement terminals were proposed based on the coupling relations among factors and the real-time risk assessment mechanism, providing specialized management tools for administrators.ConclusionIn the process of industry management, more attention should be paid to the drivers' awareness of driving safety.Improve vehicle intrinsic safety through special governance and other measures, and increase the traffic control of key dangerous goods and critical time periods.Carry out targeted solutions and measures from the source of accident, reduce the probability of accidents, and improve the safety of dangerous goods road transport.