Abstract
This paper studies the short-term prediction methods of sectional passenger flow, and selects BP neural network combined with the characteristics of sectional passenger flow itself. With a case study, we design three different schemes. We use Matlab to realize the prediction of the sectional passenger flow of the Beijing subway Line 2 and make comparative analysis. The empirical research shows that combining data characteristics of sectional passenger flow with the BP neural network have good prediction accuracy.
Highlights
In recent years, the domestic urban rail transit develops very rapidly, for example, the future network of urban rail transit in Beijing will show a complex road network structure, high passenger demand growth and so on
The sectional passenger flow refers to the passenger flow volume through a particular place of the subway line in unit of time
The prediction of sectional passenger flow is an essential element of forecasting
Summary
The domestic urban rail transit develops very rapidly, for example, the future network of urban rail transit in Beijing will show a complex road network structure, high passenger demand growth and so on. No matter in planning, construction or operation stage, it is inseparable from the close control and forecast of the passenger flow. Short-term forecast for sectional passenger flow is a very important part. The research of forecasting methods is very necessary. This text will combine the Beijing subway Line 2 to study short-term sectional passenger flow forecasting methods
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