Passenger flow prediction method and system based on attention mechanism and rclstm network
A prediction method and attention technology, applied in prediction, biological neural network models, data processing applications, etc., can solve problems such as time-consuming, insufficient model processing of strong nonlinear features, low prediction accuracy, etc., to achieve low accuracy, The effect of short training time and reduced complexity
Active Publication Date: 2022-04-08
HEFEI UNIV OF TECH
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Problems solved by technology
Short-term passenger flow forecasting has very strong nonlinear characteristics, and the current model's ability to deal with strong nonlinear characteristics is far from enough
Moreover, passenger flow forecasting is affected by many factors, and the importance and degree of influence of each factor are different, and the importance of different time periods is also different. All current models do not take this issue into account. May lead to poor prediction accuracy
In addition, it will take a lot of time to train the model due to the large amount of calculation and other reasons.
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[0045] The specific implementation manners of the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation manners described here are only used to illustrate and explain the implementation manners of the present invention, and are not intended to limit the implementation manners of the present invention.
[0046] In the embodiments of the present invention, unless stated otherwise, the used orientation words such as "up, down, top, bottom" are usually for the directions shown in the drawings or for vertical, vertical or The term used to describe the mutual positional relationship of each component in terms of the direction of gravity.
[0047] In addition, if there are descriptions involving "first", "second" and so on in the embodiments of the present invention, the descriptions of "first", "second" and so on are only for descriptive purposes, and should not b...
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The embodiment of the present invention provides a passenger flow prediction method and system based on attention mechanism and RCLSTM network, belonging to the technical field of data mining. The prediction method comprises: respectively generating multiple RCLSTM networks with different connection probabilities under uniform distribution, normal distribution, and F distribution; adopting a training method in conjunction with an attention mechanism to train each of the RCLSTM networks; Calculate the objective function value of each RCLSTM network according to formula (1), and select the RCLSTM network with the largest objective function to predict the passenger flow. The prediction method and system can accurately predict the future passenger flow data of the scenic spot based on the historical passenger flow data of the scenic spot.
Description
technical field [0001] The present invention relates to the technical field of data mining, in particular to a passenger flow prediction method and system based on an attention mechanism and an RCLSTM network. Background technique [0002] The short-term passenger flow forecasting of scenic spots is one of the key issues in the management of current scenic spots. The current short-term passenger flow forecasting models of scenic spots include traditional time series models, BP neural network models, support vector machines, etc., and models optimized by algorithms . Short-term passenger flow forecasting has very strong nonlinear characteristics, and the current model's ability to deal with strong nonlinear characteristics is far from enough. Moreover, passenger flow forecasting is affected by many factors, and the importance and degree of influence of each factor are different, and the importance of different time periods is also different. All current models do not take th...
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IPC IPC(8): G06Q10/04G06Q50/14G06N3/04
CPCG06Q10/04G06Q50/14G06N3/049G06N3/044
Inventor 陆文星金捷钰梁昌勇董骏峰蒋丽赵树平周秀娜冉家敏
Owner HEFEI UNIV OF TECH