Aiming at the application risks caused by the performance degradation of the existing deterministic optimization methods under the condition of passenger flow uncertainty, a distributionally robust optimization method for the coordination of train and passenger flows in regional rail transit system, incorporating train skip-stop strategy, is proposed. Initially, suitable skip-stop lines and patterns are selected based on the passenger flow throughput, leading to the construction of a deterministic optimization model that considers the coordination of train and passenger flows with skip-stop operation. A distributionally robust optimization approach is then employed, utilizing ambiguity sets to describe the uncertainty of passenger demand, resulting in the development of a distributionally robust optimization model. This model is further refined with prior holiday information to adjust the ambiguity sets. The optimization model is solved using the genetic algorithm, and validation is conducted using desensitized real data from the Chongqing regional rail transit system. Experimental results indicate that the skip-stop strategy can further reduce the global transport capacity risk of the system. Compared to the deterministic model, the distributionally robust optimization model exhibits superior performance under conditions of uncertainty, and the optimization performance is further enhanced by the introduction of prior information.
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.
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