Scenario construction and deduction for railway emergency response decision-making based on network models

Railway emergencies have the characteristics of unobvious precursors and complex secondary derivatives, which is difficult for decision-makers to make effective emergency response solutions. This paper develops a scenario-response method to construct and deduce the state and severity of railway emergencies. Firstly, the scenario framework is constructed and the key scenario elements are extracted. Secondly, three scenario deduction models based on dynamic Bayesian network, fuzzy neural network, and convolutional neural network are proposed to deduce the process of an emergency. Finally, the “7.23” Yongwen train collision accident as a case study is presented and discussed. The results show that convolutional neural network model with multi time-sequence inputs has an advantage of high accuracy in comparisons with the dynamic Bayesian network model and fuzzy neural network model.