Research of High-Speed Railway Running Economic Benefit Based on Gray Model

2012 ◽  
Vol 253-255 ◽  
pp. 1273-1277
Author(s):  
Xue Dong Du ◽  
Na Ren
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Economic Benefit ◽  
High Speed ◽  
Accurate Prediction ◽  
Gray Model ◽  
Practical Application ◽  
High Speed Railway ◽  
Railway Stations ◽  
Train Operation

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.

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

2012 ◽  
Vol 569 ◽  
pp. 246-250 ◽  
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.

scholarly journals Analysis of ATO System Operation Scenarios Based on UPPAAL and the Operational Design Domain

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 503
Author(s):  
Zicong Meng ◽  
Tao Tang ◽  
Guodong Wei ◽  
Lei Yuan
Keyword(s):  
High Speed ◽  
Test Case ◽  
Design Domain ◽  
System Operation ◽  
High Speed Railway ◽  
Functional Specification ◽  
Operational Design ◽  
Train Operation ◽  
System Requirements ◽  
Automatic Train Operation

With the gradual maturity of the automatic train operation (ATO) system in subways, its application scope has also expanded to the high-speed railway field. Considering that the ATO system is still in the early stages of operation, it will take time to fully mature, and definite specifications of the requirements for system operation have not yet been formed. This paper presents the operational design domain (ODD) of the high-speed railway ATO system and proposes a scenario analysis method based on the operational design domain to obtain the input conditions of the system requirements. The article models and verifies the scenario of the linkage control of the door and platform door based on the UPPAAL tools and extracts the input and expected output of the system requirements of the vehicle ATO system. Combined with the input conditions of the system requirements, the system requirements of the vehicle ATO in this scenario are finally obtained, which provides a reference for future functional specification generation and test case generation.

Notch-based speed trajectory optimisation for high-speed railway automatic train operation

2021 ◽  
pp. 095440972110421
Author(s):  
Minling Feng ◽  
Chaoxian Wu ◽  
Shaofeng Lu ◽  
Yihui Wang
Keyword(s):  
High Speed ◽  
Applied Force ◽  
Mixed Integer ◽  
High Speed Railway ◽  
Train Operation ◽  
Train Speed ◽  
Automatic Train Operation ◽  
The Impact ◽  
Trajectory Optimisation

Automatic train operation (ATO) systems are fast becoming one of the key components of the intelligent high-speed railway (HSR). Designing an effective optimal speed trajectory for ATO is critical to guide the high-speed train (HST) to operate with high service quality in a more energy-efficient way. In many advanced HSR systems, the traction/braking systems would provide multiple notches to satisfy the traction/braking demands. This paper modelled the applied force as a controlled variable based on the selection of notch to realise a notch-based train speed trajectory optimisation model to be solved by mixed integer linear programming (MILP). A notch selection model with flexible vertical relaxation was proposed to allow the traction/braking efforts to change dynamically along with the selected notch by introducing a series of binary variables. Two case studies were proposed in this paper where Case study 1 was conducted to investigate the impact of the dynamic notch selection on train operations, and the optimal result indicates that the applied force can be flexibly adjusted corresponding to different notches following a similar operation sequence determined by optimal train control theory. Moreover, in addition to the maximum traction/braking notches and coasting, medium notches with appropriate vertical relaxation would be applied in accordance with the specific traction/braking demands to make the model feasible. In Case study 2, a comprehensive numerical example with the parameters of CRH380AL HST demonstrates the robustness of the model to deal with the varying speed limit and gradient in a real-world scenario. The notch-based model is able to obtain a more realistic optimal strategy containing dynamic notch selection and speed trajectory with an increase (1.622%) in energy consumption by comparing the results of the proposed model and the non-notch model.

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

2017 ◽  
Vol 873 ◽  
pp. 220-224 ◽  
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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