High-Speed Railway Passenger Flow Volume Prediction Model Based on Factor-Response Simulation Method

2011 ◽  
Vol 10 (9) ◽  
pp. 1761-1766 ◽  
Author(s):  
Du Xuedong ◽  
Shang Rui ◽  
Gao Ziyou
Keyword(s):  
Prediction Model ◽  
High Speed ◽  
Simulation Method ◽  
Flow Volume ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Model Based ◽  
Railway Passenger

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.

Short-Time Fluctuation Characteristic and Combined Forecasting of High-Speed Railway Passenger Flow Based on EEMD

2013 ◽  
Vol 409-410 ◽  
pp. 1071-1074
Author(s):  
Xiu Shan Jiang ◽  
Rui Feng Zhang ◽  
Liang Pan
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Mode Decomposition ◽  
Railway Passenger ◽  
Short Term Forecasting

Take Wuhan-Guangzhou high-speed railway for example. By adopting the empirical mode decomposition (EMD) attempt to analyze mode from the perspective of volatility of high speed railway passenger flow fluctuation signal. Constructed the ensemble empirical mode decomposition-gray support vector machine (EEMD-GSVM) short-term forecasting model which fuse the gray generation and support vector machine with the ensemble empirical mode decomposition (EEMD). Finally, by the accuracy of predicted results, explains the EEMD-GSVM model has the better adaptability.

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

2014 ◽  
Vol 505-506 ◽  
pp. 632-636 ◽  
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.

The Changing of Passenger Flow Volume and Distribution of Sharing when High Speed Railway Speed Reducing

2015 ◽  
Vol 744-746 ◽  
pp. 2180-2184
Author(s):  
Ying Wang ◽  
Bao Ming Han ◽  
Bin Zheng
Keyword(s):  
Public Transport ◽  
Logit Model ◽  
High Speed ◽  
Influence Factors ◽  
High Speed Railway ◽  
Public Transport System ◽  
Passenger Flow ◽  
Transportation Corridor ◽  
Before And After ◽  
Railway Passenger

Reduction of high speed railway speed has an influence on not only high speed railway passenger flow but also passenger flow of the public transport system within the scope of relevant transportation corridor. Calculate method of percentage of passenger transport with a variety of public transport mode using Logit model is presented. And analysis of Wuhan Guangzhou high speed railway passenger flow changes before and after reducing speed is introduced. Modify Logit model making it can be applied to the passenger flow partaking rates to meet the changing trend of passenger flow. Bring in new ideas of using Logit to calculate the distribution rate of passenger flow and provide reliable and effective solutions. To provide reference for a more scientific way of express passenger flow partaking rates for development of passenger flow law and analyzing of China's current influence factors.

Railway Passenger Flow Volume Prediction Model Analysis Based on Cobb-Douglas Function

2012 ◽  
Vol 178-181 ◽  
pp. 1961-1964 ◽  
Author(s):  
Xue Dong Du ◽  
Na Ren
Keyword(s):  
Prediction Model ◽  
Error Rate ◽  
Model Analysis ◽  
Flow Volume ◽  
Passenger Flow ◽  
Economic Indexes ◽  
Railway Passenger ◽  
Industrial Economic

A prediction model of train passenger flow volume is proposed in this article to help the railway administration's analysis of running strategies. The model is analysed based on industrial economic indexes and Cobb-Douglas theory to make the prediction. The model is illustrated by applying it to a numeral example, and the analysis of the error rate is made for further research.

scholarly journals An Affinity Propagation-Based Clustering Method for the Temporal Dynamics Management of High-Speed Railway Passenger Demand

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenxian Wang ◽  
Tie Shi ◽  
Yongxiang Zhang ◽  
Qian Zhu
Keyword(s):  
High Speed ◽  
Temporal Dynamics ◽  
Operation Time ◽  
Affinity Propagation ◽  
High Speed Railway ◽  
Railway Line ◽  
Passenger Flow ◽  
Passenger Demand ◽  
Train Operation ◽  
Railway Passenger

The number of passengers in a high-speed railway line normally varies significantly by the time periods, such as the peak and nonpeak hours. A reasonable classification of railway operation time intervals is essential for an adaptive adjustment of the train schedule. However, the passenger flow intervals are usually classified manually based on experience, which is subjective and inaccurate. Based on the time samples of actual passenger demand data for 365 days, this paper proposes an affinity propagation (AP) algorithm to automatically classify the passenger flow intervals. Specifically, the AP algorithm first merges time samples into different categories together with the passenger transmit volume of the stations, which are used as descriptive variables. Furthermore, clustering validity indexes, such as Calinski–Harabasz, Hartigan, and In-Group Proportion, are employed to examine the clustering results, and reasonable passenger flow intervals are finally obtained. A case study of the Zhengzhou-Xi’an high-speed railway indicates that our proposed AP algorithm has the best performance. Moreover, based on the passenger flow interval classification results obtained using the AP algorithm, the train operation plan fits the passenger demand better. As a result, the indexes of passenger demand satisfaction rate, average train occupancy rate, and passenger flow rate are improved by 7.6%, 16.7%, and 14.1%, respectively, in 2014. In 2015, the above three indicators are improved by 5.7%, 18.4%, and 14.4%, respectively.

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