To grasp the trend of safety conditions in the operation process of multisystem rail transit, and allocate transport capacity and maintenance resources reasonably, the managers should master accurate and comprehensive safety evaluation of regional rail transit system. A data-driven model for safety evaluation of regional rail transit system was proposed in this study. The deep autoencoder networks were employed to reduce the dimensions of the evaluation index system. The hybrid hierarchical k-means clustering method was applied to obtain the set of all possible safety status. The tree-augmented naïve Bayes algorithm was used to evaluate the overall safety. The validity and practicality of the model were verified using actual operations data from a rail transit network in regional urban agglomeration in China. A comparison with the actual situation shows that the proposed approach can evaluate the safety level of the network effectively.
Evaluating Regional Rail Transit Safety: A Data-Driven Approach
Lect. Notes Electrical Eng.
International Conference on Electrical and Information Technologies for Rail Transportation ; 2023 ; Beijing, China October 19, 2023 - October 21, 2023
Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023 ; Chapter : 68 ; 671-681
2024-02-03
11 pages
Article/Chapter (Book)
Electronic Resource
English
Data-Driven Safety Model on Urban Rail Transit Signal System
Springer Verlag | 2019
|Data-Driven Safety Model on Urban Rail Transit Signal System
TIBKAT | 2019
|