Urban population flow prediction method based on double-path space-time residual error network

Abstract

The invention fully utilizes space-time characteristics of urban population flow data, and on the basis of residual error network structures ResNet and DenseNet, provides an urban population flow prediction method based on a double-path space-time residual error network. The method adopts the double-path space-time residual error network to perform urban population flow prediction, and can improvearea population flow-in and flow-out prediction performance. The double-path space-time residual error network includes a plurality of study modules; each study module consists of micro blocks, eachmicro block starts from a first convolutional layer, is then connected with a second convolutional layer, and ends with a third convolutional layer; and outputs of the third convolutional layer include a first output and a second output, the first output is added to an original signal-channel residual error network according to a corresponding element adding method, and the second output is connected with a path of a dense connection network. The urban population flow prediction method based on a double-path space-time residual error network utilizes the space-time characteristics of urban population flow data, and on the basis of the residual error network structures ResNet and DenseNet, designs a double-path residual error network ST-DPResNet, thereby further improving the area population flow-in and flow-out prediction performance.

Description

technical field [0001] The invention relates to a method for predicting the flow of urban crowds, in particular to a method for predicting the flow of urban crowds based on a dual-path space-time residual network. Background technique [0002] The advent of the era of big data provides more opportunities and broader prospects for urban computing. How to use big data to improve the intelligence and refinement of urban management and social governance has become a research hotspot today. Accurately predicting the flow of people in a region in the future is of great significance for urban traffic management, risk assessment, public safety and other fields. [0003] Existing research on urban crowd flow prediction focuses on entities such as people, vehicles, and roads, such as individual movement trajectory prediction and short-term traffic flow prediction. These predictions are an important perspective in urban computing, enabling the analysis of movement patterns of individ...

AI Technical Summary

Problems solved by technology

However, the traditional single-path residual network structure does not make full use of the spatio-temporal data characteristics, and there are deficiencies in convergence, which affects the training effect of the data.

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