Passenger flow prediction method based on time-lag NARX neural network

A technology of neural network and forecasting method, which is applied in the field of rail passenger flow forecasting, can solve the problems of low accuracy rate, achieve the effect of improving accuracy and accuracy, reducing the amount of calculation and difficulty of forecasting

Inactive Publication Date: 2017-08-18
CHONGQING UNIV
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Problems solved by technology

[0004] The technical problem to be solved by the prese...

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Embodiment 1

[0052] A method for predicting passenger flow based on a time-delay NARX neural network, characterized in that: the method comprises:

[0053] (1) Collect n pieces of historical data from the automatic fare collection system as original samples, and preprocess the original samples to obtain preprocessed samples;

[0054] (2) According to the preprocessing samples in step (1), according to the nonlinear autoregressive network with external input, establish the NARX short-term passenger flow forecasting model p(t) about the time series, and the external input is the external impression factor u (t):

[0055] p(t)=f(p(t-1),p(t-2),...,p(t-n),u(t-1),u(t-2),...,u( t-n), W)

[0056] =f[p(t),u(t),W],

[0057] The NARX short-term passenger flow prediction model p(t) adds an external input u(t) closely related to the training samples;

[0058] (3) according to the NARX short-term passenger flow prediction model in the step (2), and training algorithm, carry out real-time passenger f...

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Abstract

The invention relates to a passenger flow prediction method based on a time-lag NARX neural network and mainly aims to solve the technical problem of low prediction precision in the prior art. The method comprises the steps that n pieces of historical data is collected from an automatic fare collection system to serve as original samples, and preprocessing is performed to obtain preprocessed samples; an NARX short-time passenger flow prediction model p(t) about a time sequence is established according to a nonlinear autoregression network with external input, wherein the external input is an external impression factor u(t), and the nonlinear autoregression network with the external input comprises an input layer, input lag time, a hidden layer, an output layer and output lag time; and real-time passenger flow prediction is performed according to the NARX short-time passenger flow prediction model, an excitation function and a training algorithm, wherein real-time passenger flow prediction comprises short-time passenger flow prediction, peak prediction and representative passenger flow distribution site prediction. Through the technical scheme, the technical problem is solved, and the method can be applied to rail passenger flow prediction.

Description

technical field [0001] The invention relates to the field of rail passenger flow forecasting, in particular to a passenger flow forecasting method based on a time-delay NARX neural network. Background technique [0002] With the rapid development of urbanization, the conflict between the travel demand of the urban population and the urban traffic carrying capacity has become more and more prominent. With its unique advantages of high speed, high capacity and environmental protection, urban rail transit stands out from various transportation modes and becomes the main means of transportation to solve traffic congestion. Cities have invested in construction one after another, transforming urban rail transit from single-line operation to line-network operation. While increasing its scale and complexity, it also poses challenges to the network management and development of rail transit. Fast and accurate passenger flow forecasting is not only the basis for scientifically formul...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06Q10/04G06Q50/30
CPCG06N3/08G06Q10/04G06Q50/30G06N3/048
Inventor 杨梦宁葛永新徐玲陈飞宇洪明坚黄晟王洪星李小斌许任婕赵小超
Owner CHONGQING UNIV
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