Text data-based casual analysis of initial delay of high-speed trains

Xiaofang Wang; Yong Qin

doi:10.1117/12.2684033

14 June 2023 Text data-based casual analysis of initial delay of high-speed trains

Xiaofang Wang, Yong Qin

Proceedings Volume 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）; 127080M (2023) https://doi.org/10.1117/12.2684033
Event: 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 2023, Chongqing, China

Abstract

As the railway network structure becomes more complex and capacity resources become tighter, risk factors such as equipment disruption, bad weather and human interference pose greater challenges to the on-time operation of high-speed trains. The study of the correlation between primary delay and risk factors plays an important role in improving the punctuality of trains and real-time dispatching command. Firstly, the disruptions were divided into 13 categories; secondly, a Bayesian network structure was constructed based on expert experience, and the structure was modified using a greedy thick thinning algorithm, and the expectation maximization algorithm was used for parameter learning. Finally, the TF-IDF algorithm was used to complete the keyword extraction of the text data of the emergencies that triggered delayed trains from 2016-2019, and the structured and processed 0-1 matrix data were fed into a Bayesian network for inference studies using the joint tree algorithm. The results show that the more likely types of disruptions when trains have a primary delay are, respectively, foreign object strikes or foreign invasion, contact network fault, track circuit fault, shaking or noise. The results can provide useful and valuable information for the optimization of inspection and maintenance schemes for key disruptions.

Citation Download Citation

Xiaofang Wang and Yong Qin "Text data-based casual analysis of initial delay of high-speed trains", Proc. SPIE 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 127080M (14 June 2023); https://doi.org/10.1117/12.2684033

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