A Forecast Model of Urban Passenger Flow Containing New Railway Project

2008 ◽  
Author(s):  
Ya Wen ◽  
Kefei Yan
Keyword(s):  
Forecast Model ◽  
Passenger Flow

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.

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.

scholarly journals Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Fei Dou ◽  
Limin Jia ◽  
Li Wang ◽  
Jie Xu ◽  
Yakun Huang
Keyword(s):  
Temporal Logic ◽  
High Speed ◽  
Nearest Neighbor ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Railway Transportation ◽  
Real World Data ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Flow Forecast

Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than thek-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.

Prediction of Urban Rail Transit Sectional Passenger Flow Based on Elman Neural Network

2014 ◽  
Vol 505-506 ◽  
pp. 1023-1027 ◽  
Author(s):  
Qian Li ◽  
Yong Qin ◽  
Zi Yang Wang ◽  
Zhong Xin Zhao ◽  
Ming Hui Zhan ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Elman Neural Network ◽  
Passenger Flow ◽  
Urban Rail ◽  
Modeling Experiment ◽  
Beijing Subway

This paper based on the feature of Beijing urban rail transit sectional passenger flow, combined with Elman neural network. After carrying out modeling experiment many times, a reasonable forecast model about the prediction of urban rail transit sectional passenger flow was established. Then the Elman neural network model was used to predict the sectional passenger flow of Beijing Subway Line 1, from Xidan station to Fuxingmen Station. At last the output results was compared with that of BP neural network, the result shows that the Elman neural network is more precise and effective than the BP neural network in the prediction of urban rail transit sectional passenger flow.

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