INFLUENCING FACTORS OF HIGH-SPEED RAILWAY PASSENGERS’ TRAVEL CHOICE BASED ON ROUGH SET

2011 ◽  
Keyword(s):  
Influencing Factors ◽  
Rough Set ◽  
High Speed ◽  
High Speed Railway ◽  
Travel Choice

Highway Passenger Transport Development Strategy with the Competition of High-Speed Railway

2011 ◽  
Vol 94-96 ◽  
pp. 527-530
Author(s):  
Si Tao Hu ◽  
Xue Mei Wang
Keyword(s):  
Development Strategy ◽  
High Speed ◽  
Choice Behavior ◽  
Rapid Development ◽  
Technical Support ◽  
Passenger Transport ◽  
High Speed Railway ◽  
Travel Choice ◽  
Support Resource ◽  
The Impact

With the rapid development of high-speed passenger railway and inter-city railway, highway passenger transport is facing an unprecedented competition and pressure. The impact of high-speed railway on highway passenger transport has been analysis and the transport characteristics of high-speed railway are compared with the highways’. On the basis of the travel choice behavior of passengers analysis, the systematized countermeasures of highway passenger transport are put forward including precise orientation, strengthen marketing enhancement, technical support, resource integration, brand service and government help.

scholarly journals A rough set-based measurement model study on high-speed railway safety operation

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0197918 ◽  
Author(s):  
Qizhou Hu ◽  
Minjia Tan ◽  
Huapu Lu ◽  
Yun Zhu
Keyword(s):  
Rough Set ◽  
High Speed ◽  
Measurement Model ◽  
High Speed Railway ◽  
Model Study ◽  
Railway Safety ◽  
Safety Operation

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

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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