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Short-term traffic flow prediction method based on deep learning

A technology of short-term traffic flow and deep learning, which is applied in the field of short-term traffic flow prediction based on deep learning to achieve the effect of improving the prediction effect.

Active Publication Date: 2020-05-29
BEIJING UNIV OF TECH
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Problems solved by technology

[0007] In order to solve the problem that the existing short-term traffic flow prediction method fails to make full use of the spatio-temporal characteristics of traffic flow data to achieve accurate prediction, this invention proposes a short-term traffic flow prediction method based on deep learning

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  • Short-term traffic flow prediction method based on deep learning
  • Short-term traffic flow prediction method based on deep learning
  • Short-term traffic flow prediction method based on deep learning

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Embodiment Construction

[0040] The steps of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0041] Step 1. Traffic flow data preprocessing.

[0042] Firstly, the traffic flow data of the observation point at all times is obtained, and then the maximum and minimum normalization processing is performed on all the traffic flow data. The calculation formula is:

[0043]

[0044] where x max and x min Respectively represent the maximum value and minimum value of traffic flow data at all moments of the observation point, x is the traffic flow at a certain moment of the observation point, and f is the traffic flow after the maximum and minimum normalization of x.

[0045] Traffic flow data has spatiotemporal characteristics. In terms of time, one observation point can continuously collect traffic flow data at various moments to form a time-varying traffic flow sequence; in space, multiple observation points can be set up on different road sectio...

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Abstract

The invention discloses a short-term traffic flow prediction method based on deep learning and belongs to the field of traffic prediction. The method comprises steps of firstly, extracting spatial features of traffic flow by using a convolutional neural network; secondly, extracting time features by using a gating circulation unit introducing an attention mechanism, and calculating importance of traffic flow features at different moments through the attention mechanism so that the model pays more attention to the features with high importance; extracting periodic features by using the periodicfeatures of the traffic flow data; and lastly, fusing all the features for prediction. The method is advantaged in that the defect that a prediction method in the prior art cannot fully utilize the spatial and temporal features of the traffic flow data is solved, prediction precision of the traffic flow is improved, and a problem of short-term traffic flow prediction can be better solved.

Description

technical field [0001] The invention belongs to the field of traffic prediction, and in particular relates to a short-term traffic flow prediction method based on deep learning. Background technique [0002] With the continuous increase of the number of motor vehicles in the country, the problem of urban traffic congestion is becoming more and more serious. Traffic congestion not only delays people's travel and reduces the efficiency of social activities, but also wastes a lot of resources and causes urban air pollution. In order to solve the problem of traffic congestion, Intelligent Transport System (Intelligent Transport System, ITS) came into being. ITS collects and analyzes road traffic data through the comprehensive use of big data, artificial intelligence and other technical means to improve the operating efficiency of existing traffic facilities and relieve urban traffic pressure. Traffic flow prediction is one of the core functions of ITS. [0003] Due to the tim...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G08G1/065G06N3/04G06N3/08
CPCG08G1/0129G08G1/0137G08G1/065G06N3/084G06N3/045
Inventor 李壮壮桂智明郭黎敏姚思佳
Owner BEIJING UNIV OF TECH
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