Study on the Propagation Scenario Classification of the High-speed Railway GSM-R System based on GIS

High-Speed Railway Passenger Node Classification Method and Train Stops Scheme

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.505-506.632 ◽

2014 ◽

Vol 505-506 ◽

pp. 632-636 ◽

Cited By ~ 1

Author(s):

Peng Fei Zhou ◽

Bao Ming Han ◽

Qi Zhang

Keyword(s):

High Speed ◽

Passenger Transport ◽

High Speed Rail ◽

Plan Evaluation ◽

High Speed Railway ◽

Passenger Flow ◽

Railway Passenger Transport ◽

Train Operation ◽

Railway Passenger

The development of high-speed railway has been very fast, while there are still existing many problems to be further studied and discussed, especially the design of high-speed railway Train stops program. The research of classification of high-speed passenger railway nodes has a vital significance for forecast of high-speed railway passenger flow, passenger train operation plan, evaluation and optimization and so on, especially for highspeed railway stopping schedule .This paper analyzes the significance and methods of high-speed passenger railway nodes classification, and designs high-speed rail train line stops program based on the classification. Finally, analyzing the case on the basis of Beijing-Guangzhou high-speed railway, a train stops program will be made bases on the classification of Beijing-Guangzhou high-speed railway passenger transport nodes to verify the feasibility of this study.

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Classification of High-Speed Railway Network Transfer Nodes Based on the Improved Gray Whitenization Weight Function Clustering Method

Advances in Smart Vehicular Technology, Transportation, Communication and Applications - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-030-04582-1_8 ◽

2018 ◽

pp. 67-75

Author(s):

Jieying Jiang ◽

Lin Wang ◽

Si Ma

Keyword(s):

Weight Function ◽

High Speed ◽

Clustering Method ◽

Railway Network ◽

High Speed Railway

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Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining

Journal of Electrical and Computer Engineering ◽

10.1155/2021/7146435 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Fan Gao ◽

Fan Li ◽

Zhifei Wang ◽

Wenqi Ge ◽

Xinqin Li

Keyword(s):

Text Mining ◽

High Speed ◽

Single Layer ◽

Feature Representation ◽

Classification Model ◽

High Speed Railway ◽

Railway Signal ◽

Representation Method ◽

Multilevel Classification

In this paper, the multilevel classification model of high-speed railway signal equipment fault based on text mining technology is proposed for the data of high-speed railway signal fault. An improved feature representation method of TF-IDF is proposed to extract the feature of fault text data of signal equipment. In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary learners, and k-fold cross verification was used to train the stacking model. The multitask cooperative voting decision tree was designed to correct the membership relationship of classification results of each layer. Ten years of signal switch machine fault data of high-speed railway are used for experimental analysis; the experiment shows that the multilevel classification model can effectively improve the classification of signal equipment fault multilevel classification task evaluation index and can ensure the correctness of the subordinate relations’ classification results.

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