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

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...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is the problem of low accuracy in the prior art

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