City large-scale road network traffic speed prediction method based on deep integrated learning

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

Active Publication Date: 2019-10-01
ZHEJIANG UNIV
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  • Abstract
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  • Application Information

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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 predi

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  • City large-scale road network traffic speed prediction method based on deep integrated learning
  • City large-scale road network traffic speed prediction method based on deep integrated learning
  • City large-scale road network traffic speed prediction method 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 invention relates to a city large-scale road network traffic speed prediction method based on deep integrated learning. The method comprises the following main steps: acquiring traffic flow detection data of all detection points in a road network; decomposing a speed time sequence into a plurality of intrinsic mode functions and residual sequences; adding an external variable to establish a three-dimensional space-time depth input tensor, stacking a detector on a third dimension depth dimension; calibrating parameters of a convolutional neural network model, performing prediction for a matrix composed of the intrinsic mode functions and the residual sequences through the calibrated model; reestablishing a predicted speed time subsequence, restoring the subsequence to be a predicted speed time sequence of all the detection points of a road network level. In the method provided by the invention, a complicated nonlinear and non-steady speed time sequence is decomposed into a pluralityof subsequences with higher periodicity, one-time multi-step prediction of city large-scale road network traffic speed is realized, meanwhile, prediction precision and prediction efficiency are improved, and the method has good spatial extensibility.

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...

Claims

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Application Information

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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
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