Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 22, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Graph convolution networks based on adaptive spatiotemporal attention for traffic flow forecasting.

Hongbo Xiao^1,2,3,4, Beiji Zou^5,6, Jianhua Xiao^7,8

¹School of Computer Science and Engineering, Central South University, Changsha, 410083, China.

Scientific Reports

|March 16, 2025

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Retraction notice to "Myeloid-derived suppressor cells accumulate in the liver site after sepsis to induce immunosuppression" Cellular Immunology 279 (2012) 12-20.

Cellular immunology·2026

Same author

Fabrication of Dimeric Prodrug Self-Assembly for Efficient Utilization of Aldehyde-Containing Plant Essential Oils in Management of Plant Disease.

ACS applied materials & interfaces·2026

Same author

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

The Impact of Climate Change on the Climatic Suitability of <i>Rhipicephalus microplus</i> in Mainland China.

Vector borne and zoonotic diseases (Larchmont, N.Y.)·2026

Same author

Long-term photovoltaic succession promotes preferential ammonium accumulation in desert ecosystems.

iScience·2026

Same author

Feature decoupling and cross domain alignment with transfer learning for cross working condition mechanical fault diagnosis.

Scientific reports·2026

Same journal

A tri-axis optomechanical accelerometer with plasmonic MIM waveguide and structural direction-dependent optical signatures.

Scientific reports·2026

Same journal

Holographic leaky-wave antennas with independently controlled multiple counter-rotating vortex beams.

Scientific reports·2026

Same journal

Differential associations of longitudinal hearing and vision trajectories with dementia and mild cognitive impairment in older adults.

Scientific reports·2026

Same journal

Abdominal obesity and leisure-time sedentary behavior in relation to gastroesophageal reflux disease risk: a prospective cohort study from the UK Biobank.

Scientific reports·2026

Same journal

Effect of nitrogen-rich COF incorporation on the structure and separation performance of polyamide nanofiltration membranes.

Scientific reports·2026

Same journal

Withanolide A inhibits hIAPP aggregation: An In silico, biophysical, and drosophila-based In vivo validation.

Scientific reports·2026

See all related articles

This summary is machine-generated.

This study introduces a novel graph convolutional traffic flow prediction model that enhances accuracy by considering road network topology. The model effectively captures dynamic spatiotemporal correlations for improved intelligent transportation systems.

Area of Science:

Intelligent Transportation Systems
Traffic Engineering
Machine Learning for Transportation

Background:

Accurate traffic flow prediction is crucial for intelligent transportation systems.
Existing methods often neglect road topology's impact on spatiotemporal traffic dynamics.
Traffic flow exhibits complex nonlinearity, dynamic changes, and spatiotemporal dependencies.

Purpose of the Study:

To propose a novel graph convolutional traffic flow prediction model.
To integrate road network topology into traffic flow prediction.
To improve the accuracy of traffic flow forecasting.

Main Methods:

Developed a graph convolutional traffic flow prediction model incorporating adaptive spatiotemporal attention.
Utilized graph convolutional networks (GCNs) and long short-term memory (LSTM) networks for spatiotemporal feature extraction.

Keywords:

GCN LSTM Spatiotemporal Attention Mechanism Traffic Flow Prediction

More Related Videos

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

Related Experiment Videos

Last Updated: May 22, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

Introduced a fusion mechanism to combine spatiotemporal data features with road network topology.

Main Results:

The proposed model adaptively adjusts spatiotemporal weights to capture dynamic correlations.
The model effectively integrates spatial topological relationships of the road network.
Experimental results show superior performance compared to six baseline methods.

Conclusions:

The proposed graph convolutional model with adaptive spatiotemporal attention significantly enhances traffic flow prediction accuracy.
Accounting for road topology is vital for capturing complex spatiotemporal traffic characteristics.
This approach offers a promising advancement for intelligent transportation systems.