Research on an adaptive mechanism for railway passenger flow demand prediction

2015 ◽  
Author(s):  
Wei Zhengzheng ◽  
Wang Fuzhang ◽  
Shan Xinghua ◽  
Lv Xiaoyan
Keyword(s):  
Adaptive Mechanism ◽  
Demand Prediction ◽  
Passenger Flow ◽  
Flow Demand ◽  
Railway Passenger

scholarly journals An Approach for Real-time Estimation of Railway Passenger Flow

2010 ◽  
Vol 51 (2) ◽  
pp. 82-88 ◽  
Author(s):  
Yoichi SUGIYAMA ◽  
Hiroshi MATSUBARA ◽  
Shuichi MYOJO ◽  
Kazuki TAMURA ◽  
Naoya OZAKI
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Passenger Flow ◽  
Real Time Estimation ◽  
Railway Passenger

The Research of Railway Passenger Flow Prediction Model Based on BP Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2366-2369 ◽  
Author(s):  
Yao Wang ◽  
Dan Zheng ◽  
Shi Min Luo ◽  
Dong Ming Zhan ◽  
Peng Nie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Good Effect ◽  
Passenger Flow ◽  
Model Based ◽  
Flow Prediction ◽  
Transport Organization ◽  
Railway Passenger ◽  
Short Time

Based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow, the forecast model of railway short-term passenger flow based on BP neural network was established. This paper mainly researches on fluctuation characteristics and short-time forecast of holiday passenger flow. Through analysis of passenger flow and then be used in passenger flow forecasting in order to guide the transport organization program especially the train plan of extra passenger train. And the result shows the forecast model based on BP neural network has a good effect on railway passenger flow prediction.

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 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.

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.

Application of Data Mining in the Forecasting of Railway Passenger Flow

2013 ◽  
Vol 834-836 ◽  
pp. 958-961 ◽  
Author(s):  
Dan Zheng ◽  
Yao Wang ◽  
Peng Zhi Tang ◽  
Yan Ping Wu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Warehouse ◽  
Flow Analysis ◽  
Forecast Model ◽  
Analysis Model ◽  
Data Mart ◽  
Passenger Flow ◽  
Network Method ◽  
Railway Passenger

This paper through studying the theory of data warehouse and data mining, applies these technologies to deal with the large number data in the Ticket Selling and Reserving System of Chinese Railway (TRS), uses the effective data mining to the passenger flow analysis, builds up the logical forecasting and analysis model. This paper firstly discusses the current situation and problems faced by forecasting of passenger flow, then applies the data warehouse technology to design the data mart of this subject. Next, samples and analyses this data which collecting in data mart adopting neural network method, builds data analysis model carrying out research and the experiment, finally puts forward a feasible forecast model for the passenger flow forecasting.

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