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A traffic speed prediction method for urban large-scale road network based on deep integrated learning

An integrated learning and speed prediction technology, applied in neural learning methods, traffic flow detection, road vehicle traffic control systems, etc., to achieve the effect of improving prediction accuracy, strong periodicity, and good spatial scalability

Active Publication Date: 2020-11-17
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The method of the invention is simple and effective, easy to operate, overcomes the impact of traffic speed time series noise on the prediction results, solves the limitation of the application range of the existing traffic prediction method, and has good spatial expandability

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  • A traffic speed prediction method for urban large-scale road network based on deep integrated learning
  • A traffic speed prediction method for urban large-scale road network based on deep integrated learning
  • A traffic speed prediction method for urban large-scale road network based on deep integrated learning

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Embodiment Construction

[0020] This invention is based on the research of National Key Research and Development Project (2018YFB1600904), National Natural Science Foundation Project (71771198, 71961137005) and Zhejiang Provincial Natural Science Foundation Outstanding Youth Project (LR17E080002), and involves a large-scale urban road network based on deep integrated learning The traffic speed prediction method, the present invention will be further described below in conjunction with specific embodiments, but the protection scope of the present invention is not limited thereto.

[0021] Taking the second, third, fourth ring roads and their radiation roads in Beijing as an example, there are 308 detectors in total, the road network covers an area of ​​about 300 square kilometers, and the total road length is about 360 kilometers. The topological structure of the road section and the layout position of the detector are as follows: figure 1 shown. The short-term prediction of the traffic speed of the r...

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Abstract

The present invention relates to a large-scale urban road network traffic speed prediction method based on deep integrated learning. The main steps include: obtaining traffic flow detection data of all detection points in the road network; decomposing the speed time series into multiple eigenmodes function and residual sequence; add external variables to construct a three-dimensional space-time depth input tensor, and stack the detectors on the third-dimensional depth dimension; calibrate the parameters of the convolutional neural network model, and use the calibrated model to pair the intrinsic mode function and The matrix formed by the residual sequence is used for prediction; the predicted speed time subsequence is reconstructed to restore the predicted speed time series of all detection points at the road network level. The method of the invention decomposes the complex nonlinear and non-stationary speed time series into multiple sub-sequences with stronger periodicity, and improves the prediction accuracy and Prediction efficiency, with good spatial expansion.

Description

technical field [0001] The invention relates to the field of road network traffic speed prediction, in particular to a large-scale urban road network traffic speed prediction method based on deep integrated learning. Background technique [0002] In the urban road traffic system, speed is the most intuitive indicator reflecting road users' perception of road conditions. Accurate speed prediction will help travel service providers make more accurate travel time predictions, help travelers make more reasonable travel route choices, and help governments improve traffic management efficiency. With the deployment of detection equipment such as GPS, cameras, microwaves, and geomagnetism, cities generate massive amounts of traffic data every day. How to mine and utilize these data has become an important research topic. Today, with the rapid development of the Internet age, the application range of related technologies such as big data and deep learning is becoming wider and wider...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06N3/04G06N3/08
CPCG08G1/0129G06N3/08G06N3/045
Inventor 陈喜群张帅超周凌霄于静茹姚富根莫栋
Owner ZHEJIANG UNIV