Traffic flow prediction method of deep network based on fusion of spatiotemporal features

A space-time feature and deep network technology, applied in traffic flow detection, road vehicle traffic control system, traffic control system, etc., can solve problems such as incomplete feature fusion and incomplete feature extraction

Active Publication Date: 2020-06-05
福州市联创智云信息科技有限公司
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Problems solved by technology

[0004] In view of this, the object of the present invention is to provide a traffic flow prediction method based on a deep network of fusion

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  • Traffic flow prediction method of deep network based on fusion of spatiotemporal features
  • Traffic flow prediction method of deep network based on fusion of spatiotemporal features
  • Traffic flow prediction method of deep network based on fusion of spatiotemporal features

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[0082] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0083] Please refer to figure 1 , the present invention provides a traffic flow prediction method based on a deep network of fusion spatiotemporal features, comprising the following steps:

[0084] Step A: Obtain historical traffic flow data containing spatio-temporal information, and build a traffic flow data training set;

[0085] Step B: preprocessing the traffic flow data training set to obtain the spatio-temporal matrix representation of the historical traffic flow data;

[0086] Step C: Using the space-time matrix representation as the input of the deep learning network, train the deep learning network TSNN;

[0087] Step D: Input the traffic flow data sequence to be predicted into the trained deep learning network to obtain the prediction result.

[0088] In this embodiment, the step A is specifically:

[0089] Step A1: Obtain the monitoring ...

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Abstract

The invention relates to a traffic flow prediction method of a deep network based on fusion of spatiotemporal features. The method comprises the following steps: step A, acquiring historical traffic flow data containing spatiotemporal information from a traffic platform; step B, preprocessing the historical traffic flow data containing the spatiotemporal information to obtain space-time matrix representation of the historical traffic flow data; step C, training a deep learning network TSNN by taking the space-time matrix representation as the input of the deep learning network; and step D, inputting a traffic flow data sequence to be predicted into the trained deep learning network to obtain a prediction result. According to the method, the problems of incomplete feature extraction and incomplete feature fusion in traffic flow prediction are solved, and the accuracy and the precision of traffic flow prediction are improved.

Description

technical field [0001] The invention relates to the field of deep learning and data mining, in particular to a traffic flow prediction method based on fusion of spatio-temporal features. Background technique [0002] Traffic flow prediction, as an important part of Intelligent Transportation System (ITS), plays an important role in implementing dynamic traffic control, traffic guidance, reducing traffic congestion, etc. In order to obtain accurate and timely traffic flow forecast, the early method is to use parametric model to predict traffic flow, but the parametric model usually requires stable and accurate data, while the actual traffic data is unstable and nonlinear. With the rapid development of deep learning technology, it is possible to accurately and timely predict traffic flow. The historical traffic data collected by detectors and other equipment, and processed by the computer, can predict the future traffic data. This method can obtain better prediction results b...

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Application Information

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IPC IPC(8): G08G1/01G08G1/065G06N3/04G06N3/08
CPCG08G1/0129G08G1/065G06N3/084G06N3/045
Inventor 陈锋情
Owner 福州市联创智云信息科技有限公司
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