Prediction Model of Emergency Rescue time for Special Major Accidents in High-speed Railway based on GERTS Network

The research of high-speed railway running economic benefit is important to timely know well the train operation state for the railway administration. A prediction model of high-speed railway running economic benefit is proposed in this article based on Gray model. The Gray model is a good example to make accurate prediction of the development of matters. According to the data analysis of Beijing and Shanghai railway stations, we can know that the result of prediction model is accurate, so the prediction based on Gray model is scientific and reasonable in the practical application.

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Predicting the Performance of High-Speed Railway Bridge Using Regression Neural Network Approach

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.873.220 ◽

2017 ◽

Vol 873 ◽

pp. 220-224 ◽

Cited By ~ 1

Author(s):

Young Chan Kim ◽

Mosbeh R. Kaloop ◽

Jong Wan Hu

Keyword(s):

Neural Network ◽

Prediction Model ◽

High Speed ◽

Short Term ◽

Good Tool ◽

Neural Network Approach ◽

High Speed Railway ◽

Railway Bridges ◽

Bridge Girder ◽

Artificial Neural Network Ann

The performance prediction of High-speed railway bridges (HSRB) is vital to detect the behavior of bridges under different train’s speeds. This study aims to design a prediction model using the artificial neural network (ANN) to assess the performance of Yonjung high-speed bridge. A short-term health monitoring system is used to collect the behavior of bridge with different high-speed train’s speeds. The statistical analysis is utilized to evaluate the bridge under speeds 165 to 403 Km/h. The evaluation of bridge and prediction model showing that the bridge is safe, and the ANN is shown a good tool can be used to estimate a prediction model for the displacement of bridge girder.

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High-Speed Railway PFV Prediction Model Research Under Regional Economic Conditions

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.569.246 ◽

2012 ◽

Vol 569 ◽

pp. 246-250 ◽

Cited By ~ 1

Author(s):

Xue Dong Du ◽

Na Ren

Keyword(s):

Prediction Model ◽

High Speed ◽

Economic Conditions ◽

High Speed Railway ◽

Regional Economic ◽

Railway Stations ◽

Passenger Flow ◽

Model Research ◽

Railway Passenger ◽

Railway Operation

Under the regional economic conditions, a passenger flow prediction model is proposed in the paper. It can predict high-speed railway passenger flow volume under the conditions of multi-mode, and guide the reasonable operation of high-speed railway effectively. According to the data analysis of Beijing and Tianjin railway stations, we can know that the reasonable ticket price plays an important role in high-speed railway operation benefit under regional economic conditions.

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Prediction for Percentage of Vehicle Entering Expressway Rest Area Based on BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.178-181.1956 ◽

2012 ◽

Vol 178-181 ◽

pp. 1956-1960

Author(s):

Xiao Yan Shen ◽

Hao Xue Liu ◽

Jia Liu

Keyword(s):

Neural Network ◽

Prediction Model ◽

Influencing Factors ◽

High Precision ◽

Bp Neural Network ◽

High Speed ◽

High Speed Railway ◽

Neural Network Prediction ◽

Network Prediction ◽

Influence Degree

In order to scientifically decide the percentage of vehicle entering expressway rest area, based on analyzing the influencing factors relating to the percent of mainline traffic stopping, a BP neural network prediction model for it was put forward. Finally, The Xinzheng Rest Area (XRA) was taken as an example for verifying the feasibility of the prediction model and determining the influence degree of the Shijiazhuang-Wuhan high-speed railway on the percentage of mainline vehicles entering XRA. The result shows that the model had a high precision and reliability.

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