Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

429
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
429
Transformers in Distribution System01:27

Transformers in Distribution System

165
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
165
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

296
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
296
Rapidly Varying Flow01:24

Rapidly Varying Flow

144
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
144
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

596
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
596
State Space to Transfer Function01:21

State Space to Transfer Function

311
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
311

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Novel role of Wip1 in p53-mediated cell homeostasis under non-stress conditions.

Cell cycle (Georgetown, Tex.)·2011
Same author

Globular adiponectin protects human umbilical vein endothelial cells against apoptosis through adiponectin receptor 1/adenosine monophosphate-activated protein kinase pathway.

Chinese medical journal·2011
Same author

Single-side organically functionalized Anderson-type polyoxometalates.

Chemistry (Weinheim an der Bergstrasse, Germany)·2011
Same author

Buildup of amphiphilic molecular bola from organic-inorganic hybrid polyoxometalates and their vesicle-like supramolecular assembly.

Chemistry (Weinheim an der Bergstrasse, Germany)·2011
Same author

Galphas-biased beta2-adrenergic receptor signaling from restoring synchronous contraction in the failing heart.

Science translational medicine·2011
Same author

The epidemiological, clinical, and laboratory features of sporadic Creutzfeldt-Jakob disease patients in China: surveillance data from 2006 to 2010.

PloS one·2011

Related Experiment Video

Updated: Sep 16, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K

Spatio-temporal transformer and graph convolutional networks based traffic flow prediction.

Jin Zhang1,2, Yimin Yang3, Xiaoheng Wu3

  • 1School of Computer and Information Engineering, Henan University, Kaifeng, 475004, Henan, China. zhangjin@henu.edu.cn.

Scientific Reports
|July 7, 2025
PubMed
Summary

This study introduces TDMGCN, a deep learning model for traffic flow prediction. It improves long-term forecasting and captures dynamic spatial correlations, outperforming existing methods on real-world data.

Keywords:
Long-term traffic predictionMulti graph ConvolutionSelf-attention mechanismSpatio-temporal features

More Related Videos

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.0K

Related Experiment Videos

Last Updated: Sep 16, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.0K

Area of Science:

  • Intelligent Transportation Systems
  • Deep Learning for Spatiotemporal Data Analysis

Background:

  • Traffic flow data presents complex spatiotemporal dependencies.
  • Existing methods struggle with dynamic spatial correlations and long-term prediction.

Purpose of the Study:

  • To develop a novel deep learning model, TDMGCN, for enhanced traffic flow prediction.
  • To address limitations in capturing dynamic spatial correlations and long-term temporal dependencies.

Main Methods:

  • Integrated Transformer and multi-graph Graph Convolutional Networks (GCNs).
  • Employed a convolution-based multi-head self-attention module for temporal analysis.
  • Utilized a spatial embedding module and multi-graph convolutional module for spatial analysis.
  • Incorporated periodic features of traffic flow data.

Main Results:

  • TDMGCN effectively captures long-term temporal dependencies and local trend information.
  • The model dynamically extracts complex spatial correlations using multiple graphs.
  • Experimental results on five real-world datasets show superior performance over baseline models.

Conclusions:

  • TDMGCN offers a significant advancement in traffic flow prediction accuracy.
  • The model's architecture is effective for handling complex spatiotemporal characteristics of traffic data.
  • This approach provides better decision support for intelligent transportation systems.