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Online and offline associated urban passenger flow volume prediction method

A prediction method, online and offline technology, applied in prediction, neural learning method, data processing application, etc., can solve the problem of low prediction accuracy

Active Publication Date: 2021-01-05
WUHAN UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

[0006] The present invention proposes a method for predicting urban passenger flow associated with online and offline, which is used to solve or at least partially solve the technical problem that the prior art method relies on historical data for prediction, so the prediction accuracy is not high

Method used

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  • Online and offline associated urban passenger flow volume prediction method
  • Online and offline associated urban passenger flow volume prediction method
  • Online and offline associated urban passenger flow volume prediction method

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Embodiment Construction

[0047] The embodiment of the present invention provides an online and offline urban passenger flow prediction method. On the one hand, dilated causal convolution (DCNN) is used to capture short-term, periodic, and long-term dependencies in time, and combined with external factors such as weather and holidays, On the other hand, it captures the correlation of online and offline behaviors, fully considers the impact of emergencies and online behaviors on the movement of offline user groups, and more accurately predicts the distribution of passenger flow at the next moment. .

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, ...

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Abstract

According to the online and offline associated urban passenger flow volume prediction method provided by the invention, on one hand, the time dependence of historical passenger flow volume data is fully captured, and external factor characteristics such as weather, holidays and festivals, major activity events and the like are combined; and on the other hand, the relevance of online and offline behaviors is constructed according to the online access content conditions of the user in different states in each region. After the characteristics of the two parts are fused through a trained single-hidden-layer full-connection network, the passenger flow volume overflow and overflow conditions of all regions can be more accurately predicted, and the influence of emergencies on passenger flow volume changes is fully considered.

Description

technical field [0001] The invention relates to passenger flow prediction technology related to smart city applications, big data analysis, and deep neural network technology fields, and specifically relates to an online and offline urban passenger flow prediction method. Background technique [0002] With the rapid development of Internet of Things technology, Internet of Things technology makes cities develop in a more intelligent direction. A smart city based on the Internet of Things involves a large number of densely distributed sensors in cities, heterogeneous network infrastructure, intelligent information processing, and urban computing technologies. Urban passenger flow forecasting is one of the important components for smart city applications. [0003] Currently, a wide range of smart IoT devices and services provide us with more convenience in obtaining urban crowd flow data. We can obtain a variety of security data for urban crowd detection from the interconnec...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06Q50/30G06N3/08G06N3/04G06F17/16
CPCG06Q10/04G06F17/16G06Q50/26G06N3/08G06N3/045G06Q50/40
Inventor 周蜀杰曾园园江昊
Owner WUHAN UNIV
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