A Global Transport Capacity Risk Prediction Method for Rail Transit Based on Gaussian Bayesian Network
This addresses operational risk prediction for rail transit managers, but appears incremental as it applies existing Bayesian network techniques to a specific domain.
The paper tackles the problem of predicting transport capacity risk in rail transit networks caused by mismatches between carrying capacity and passenger demand, proposing a Gaussian Bayesian Network method that was validated through simulation examples.
Aiming at the prediction problem of transport capacity risk caused by the mismatch between the carrying capacity of rail transit network and passenger flow demand, this paper proposes an explainable prediction method of rail transit network transport capacity risk based on linear Gaussian Bayesian network. This method obtains the training data of the prediction model based on the simulation model of the rail transit system with a three-layer structure including rail transit network, train flow and passenger flow. A Bayesian network structure construction method based on the topology of the rail transit network is proposed, and the MLE (Maximum Likelihood Estimation) method is used to realize the parameter learning of the Bayesian network. Finally, the effectiveness of the proposed method is verified by simulation examples.