A global transport capacity risk prediction method for rail transit based on Gaussian Bayesian network

Zhengyang Zhang; Wei Dong; Jun Liu; Xinya Sun; Yindong Ji

doi:10.1117/12.3011762

7 December 2023 A global transport capacity risk prediction method for rail transit based on Gaussian Bayesian network

Zhengyang Zhang, Wei Dong, Jun Liu, Xinya Sun, Yindong Ji

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129414D (2023) https://doi.org/10.1117/12.3011762
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhengyang Zhang, Wei Dong, Jun Liu, Xinya Sun, and Yindong Ji "A global transport capacity risk prediction method for rail transit based on Gaussian Bayesian network", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129414D (7 December 2023); https://doi.org/10.1117/12.3011762

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