Traffic data tensor filling method based on spatiotemporal constraints

A technology of traffic data and filling method, which is applied in complex mathematical operations and other directions, and can solve problems such as high data loss rate, lack of traffic information, and instability.

Active Publication Date: 2018-11-13
FUZHOU UNIV
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AI Technical Summary

Problems solved by technology

In terms of communication transmission, the information transmission of sensors and data centers is affected by the instability of the working environment and the limited transmission nodes of the wireless transmission network delay, which may lead to the loss of traffic information, and sometimes the data loss rate is extremely high

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  • Traffic data tensor filling method based on spatiotemporal constraints
  • Traffic data tensor filling method based on spatiotemporal constraints
  • Traffic data tensor filling method based on spatiotemporal constraints

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Embodiment Construction

[0039] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0040] The present invention proposes a traffic data tensor filling method based on space-time constraints, such as figure 1 As shown, the specific steps are as follows:

[0041] Step S1: Obtain incomplete traffic data and create a traffic flow data tensor;

[0042] In this embodiment, by interacting with the GPS of the vehicle, the GPS data of the detected vehicle is processed, and the average road speed data of i interconnected road sections in the area to be recovered for k days is obtained. Divide a day into j moments at equal intervals, and construct a traffic data tensor of size i×j×k Such as figure 2 shown. Incomplete tensor observed and the complete tensor to be recovered It can be represented by the following formula:

[0043]

[0044] Among them, P Ω ( ) represents a linear map, Ω is the observed subset of traffi...

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Abstract

The invention relates to a traffic data tensor filling method based on spatiotemporal constraints. The method comprises the steps of obtaining incomplete traffic data, and constructing a traffic flowdata tensor; building a tensor filling model based on factor decomposition; constructing a spatiotemporal constraint tensor by analyzing spatiotemporal characteristics of the data, and optimizing thetensor filling model based on the factor decomposition; and performing spatiotemporal constraint tensor filling, and restoring original traffic data. According to the traffic data tensor filling method based on the spatiotemporal constraints, provided by the invention, the tensor filling method based on the factor decomposition is applied to the field of traffic data restoration, and the spatiotemporal correlation and the low-rank characteristic of the traffic data are fully excavated, so that the precision of restoring the complete traffic data is improved.

Description

technical field [0001] The invention relates to a traffic data tensor filling method based on space-time constraints. Background technique [0002] With the gradual development and wide application of Internet of Things technology and big data industry, people have increasingly stringent requirements on the amount of information, accuracy and timeliness of data. As an important application scenario of the Internet of Things, the Intelligent Transportation System (Intelligent Transportation System) effectively integrates advanced information technology, data transmission technology, electronic sensing technology, control technology and computer technology into the entire ground traffic management system, thus establishing A real-time, accurate and efficient integrated traffic management system. Therefore, intelligent transportation systems also face huge challenges in data collection, processing, analysis, and utilization. [0003] In traffic information systems, data loss ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/16G06F17/14
CPCG06F17/14G06F17/16
Inventor 郑海峰林凯彤冯心欣陈忠辉魏宏安
Owner FUZHOU UNIV
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