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

A prediction method and a dual-path technology, applied in prediction, neural learning methods, biological neural network models, etc., can solve problems affecting data training effects, insufficient convergence, and insufficient use of spatio-temporal data characteristics, etc., to achieve good convergence, Good model convergence and prediction accuracy, and the effect of improving prediction performance

Inactive Publication Date: 2018-08-24
CHONGQING UNIV OF POSTS & TELECOMM
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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.

Method used

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

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Embodiment 2

[0082] In order to test the effectiveness of ST-DPResNet on the prediction of urban crowd flow, in terms of prediction accuracy and model convergence, this example is compared with the ST-ResNet method that also uses the residual network for crowd flow prediction.

[0083] Among them, the prediction accuracy is measured by the root mean square error RMSE. As shown in formula (11), x i with are the actual and predicted values, respectively, and z is the number of all predicted values. The smaller the value of RMSE, the closer the predicted value is to the actual value, and the higher the accuracy.

[0084]

[0085] Model convergence means that as the number of model iterations increases, the error decreases continuously, and the training process stops when certain conditions are finally met. The earlier the training process stops, the better the convergence.

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G06N3/08
CPCG06N3/084G06Q10/04G06Q50/26G06N3/045
Inventor 夏英刘明
Owner CHONGQING UNIV OF POSTS & TELECOMM
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