Traffic space-time sequence multi-step prediction method and system and storage medium

A multi-step forecasting and sequence technology, applied in forecasting, neural learning methods, biological neural network models, etc., can solve the problems of forecasting accuracy, difficult training, and affecting forecasting accuracy, and achieve the effect of improving forecasting accuracy

Pending Publication Date: 2021-03-16
CENT SOUTH UNIV
View PDF0 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Both IMS and DMS have different problems: on the one hand, the IMS model has no "global concept", and it only needs to be responsible for the accuracy of the next timestamp, and the prediction accuracy of the previous step will greatly affect the prediction accuracy of the next step. , which ultimately affects the overall prediction accuracy
On the other hand, the optimization goal of the DMS model involves multiple future time stamps, and the loss function needs to balance the accuracy of multiple time stamps, which is "out of reach" and difficult to train

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Traffic space-time sequence multi-step prediction method and system and storage medium
  • Traffic space-time sequence multi-step prediction method and system and storage medium
  • Traffic space-time sequence multi-step prediction method and system and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0061] Since the raw data collected by the sensor or the smart terminal cannot be directly processed by the deep learning algorithm, the embodiment of the present invention first needs to preprocess the raw data to obtain grid data that can be processed by the deep learning algorithm. Some mathematical symbols are also involved in the algorithm. In order to facilitate the description, the definitions that need to be used in the proposed algorithm are first described. These definitions refer to the literature (X.Shi and D.-Y.Yeung, "Machine learning for spatiotemporalsequence forecasting" :A survey,”arXivPrepr.arXiv1808.06865,2018. and J.Zhang,Y.Zheng,and D.Qi,”Deep spatio-temporal residual networks for citywide crowdflows prediction,” in Thirty-First AAAI Conference on Artificial Intelligence, 2017.), further elaborate the process of preprocessing.

[0062] Define 1-1 (space division) grid(i,j). like image 3 As shown, let the coordinates of point A in the lower left corner ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a traffic space-time sequence multi-step prediction method and system, and a storage medium, and the method comprises the steps: 1, calculating an improved historical mean value based on historical data X1:T; 2, carrying out the downsampling of the historical data X1:T and the improved historical mean value through a 3D-CNN module; 3, performing multi-step prediction from asequence to a sequence model to obtain an encoder and a decoder of a model, wherein the encoder and the decoder respectively consist of ConvLSTM, fusing an attention mechanism into the decoder, and further performing up-sampling by using transposed 3D convolution; 4, performing multi-model fusion to obtain a final prediction result. The prediction method provided by the invention is higher in precision.

Description

technical field [0001] The invention relates to a spatiotemporal sequence prediction method based on 3D convolutional neural network and multi-task learning, in particular to a multi-step traffic spatiotemporal sequence prediction method. Background technique [0002] The spatiotemporal sequence forecasting problems can include traffic forecasting, weather forecasting, people flow forecasting, etc. In essence, these problems are similar in that they all predict relevant spatiotemporal states within a certain time range in the future based on historical sequences. [0003] Time series methods, especially ARIMA-like models, were first applied to spatiotemporal series forecasting. Hamed et al. (M.M.Hamed, H.R.Al-Masaeid, and Z.M.B.Said, "Short-term prediction of trafficvolume in urban arterials," J.Transp.Eng., vol.121, no.3, pp.249–254, 1995 .) proposed to use the ARIMA model to predict the traffic volume on urban arterial roads. Starting with this, the researchers combined ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06F16/2458G06Q10/04G06Q50/30
CPCG06F16/2474G06N3/049G06N3/08G06Q10/04G06Q50/30G06N3/044G06N3/045
Inventor 邝砾颜学谨张欢杨海洋
Owner CENT SOUTH UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap