Traffic flow prediction method based on similar time sequence comparison

A technology of traffic flow and time series, applied in forecasting, neural learning methods, data processing applications, etc., to achieve obvious modeling effects, good prediction results, and reduced complexity

Active Publication Date: 2022-06-24
山东融瓴科技集团有限公司
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Problems solved by technology

The problem is challenging due to the complex and dynamic spatio-temporal dependencies between different regions in the road network

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  • Traffic flow prediction method based on similar time sequence comparison
  • Traffic flow prediction method based on similar time sequence comparison
  • Traffic flow prediction method based on similar time sequence comparison

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[0055] It should be noted that the following detailed description is exemplary and intended to provide further explanation of the invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0056] It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and / or combinations thereof.

[0057] Comparison of self-supervised technology introduction: Machine learning is divided into supervis...

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Abstract

The invention relates to the technical field of intelligent traffic, in particular to a traffic flow prediction method based on similar time sequence comparison, which comprises the following steps of: 1, acquiring traffic flow data of a certain region according to a public data website, and processing the traffic flow data; 2, mining a regional flow period; 3, pre-training an encoder; coding is carried out by using a deep ResNet network from a spatial angle, flow graph features are captured from the spatial angle, and due to the fact that regional flow distribution of the same city function is similar, features of similar regions are drawn close to each other by using a multi-instance contrast learning method, so that the features are far away from features of dissimilar regions; 4, the pre-trained encoder is put into the flow prediction model for fine adjustment; and 5, the model is stored. Compared with the conventional traffic flow prediction, the method has the characteristics of less parameter quantity and training cost, obvious modeling effect, good prediction result and the like.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a traffic flow prediction method based on the comparison of similar time series. Background technique [0002] With the development of data informatization, traffic forecasting plays a crucial role in the field of smart cities. Accurate traffic forecasting can assist route planning, guide vehicle scheduling, and alleviate traffic congestion. This problem is challenging due to the complex and dynamic spatiotemporal dependencies between different regions in the road network. In recent years, a lot of research work has been devoted to this field. Among them, early research focused on traditional machine learning methods. With the development of deep learning, convolutional neural networks, recurrent neural networks and feed-forward neural networks were applied to traffic flow prediction. Based on the major breakthrough of the residual structure in the field of c...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/30G06N3/04G06N3/08
CPCG06Q10/04G06Q50/30G06N3/08G06N3/045
Inventor 高文飞王辉王瑞雪郭丽丽王磊
Owner 山东融瓴科技集团有限公司
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