A Method for Forecasting Regional Radial Traffic Flow

A technology of traffic flow and forecasting method, which is applied in the direction of traffic flow detection, forecasting, traffic control system, etc., which can solve the problems of shortening the time period and achieve the effect of realizing flow and avoiding the influence of time factors

Active Publication Date: 2022-04-15
YUNNAN UNIV
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The object of the present invention is to provide a regional radial traffic flow forecasting method for the above-mentioned existing problems, which solves the problem of continuous expansion forecast based on the flow rate in the central area to the surrounding area, and shortens the time period of forecasting

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  • A Method for Forecasting Regional Radial Traffic Flow
  • A Method for Forecasting Regional Radial Traffic Flow
  • A Method for Forecasting Regional Radial Traffic Flow

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[0046] All features disclosed in this specification, or steps in all methods or processes disclosed, may be combined in any manner, except for mutually exclusive features and / or steps.

[0047] Any feature disclosed in this specification (including any appended claims, abstract), unless otherwise stated, may be replaced by alternative features which are equivalent or serve a similar purpose. That is, unless expressly stated otherwise, each feature is one example only of a series of equivalent or similar features.

[0048] Such as figure 1 Shown, a kind of regional radial traffic flow prediction method of the present invention comprises the following steps:

[0049] S1: Data collection and processing; used to collect the trajectory data information of residents’ movement in daily life, and convert the trajectory data into a traffic flow map with the smallest unit of the area according to a specific method;

[0050]S11: Clean the travel trajectory data of residents. The travel...

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Abstract

The invention discloses a regional radial traffic flow prediction method, which is characterized in that it includes the following steps: S1: collecting trajectory data information of residents' movement in daily life, and converting the trajectory data into traffic entry and exit with sub-region as the smallest unit Flow map; S2: Obtain the characteristics of traffic flow in different areas of the city, and the spatial correlation of traffic between different areas of the city; S3: Build a regional radial traffic flow prediction model, using a unique full convolutional network starting from the central area, By superimposing feature extraction-prediction sub-modules to realize gradual expansion prediction; S4: By adjusting the full convolutional neural network inversely by adjusting the error between the predicted flow map and the actual flow map, and fitting the regional radial traffic close to the real situation traffic forecasting model. The invention solves the problem of continuously extending the forecast based on the traffic flow in the central area to the surrounding area, shortens the time period of the forecast, and realizes the real-time forecast of the traffic flow.

Description

technical field [0001] The invention relates to the field of traffic flow forecasting, in particular to a regional radial traffic flow forecasting method. Background technique [0002] With the development of the economy, the number of private cars continues to increase, and the method of strengthening road infrastructure construction and increasing road traffic capacity has been unable to meet the growing traffic demand. Traffic congestion, traffic safety hazards and lack of traffic information not only bring inconvenience to travelers, but also bring resistance to urban development. The real-time grasp and accurate prediction of regional traffic flow become an important basis for solving traffic problems by using intelligent transportation technology (ITS). [0003] Most of the methods for traffic forecasting are based on time series. This type of method requires that traffic must have a great relationship with time, and the time series must be largely seasonal. The time...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06Q50/26G06Q10/06G06Q10/04G06N3/04
CPCG08G1/0129G06Q10/04G06Q10/067G06Q50/26G06N3/045
Inventor 康雁李晋源李信衍王飞胡杨卜荣景
Owner YUNNAN UNIV
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