DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting

Kyung‐Eun Lee; Wonjong Rhee

doi:10.1016/j.trc.2021.103466

ScienceGate Book Chapters

JOURNAL ARTICLE

DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting

Kyung‐Eun Lee Wonjong Rhee

Year: 2021 Journal: Transportation Research Part C Emerging Technologies Vol: 134 Pages: 103466-103466 Publisher: Elsevier BV

DOI: 10.1016/j.trc.2021.103466

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Graph Data mining Convolutional neural network Euclidean distance Spatial network Artificial intelligence Theoretical computer science Mathematics

Metrics

133

Cited By

13.19

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Traffic Prediction and Management Techniques

Physical Sciences → Engineering → Building and Construction

Transportation Planning and Optimization

Social Sciences → Social Sciences → Transportation

Human Mobility and Location-Based Analysis

Social Sciences → Social Sciences → Transportation

DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting

Metrics

Citation History

Topics

Related Documents

AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting

Spatiotemporal Graph Convolutional Network for Multi-Scale Traffic Forecasting

FDST-GCN: A Fundamental Diagram based Spatiotemporal Graph Convolutional Network for expressway traffic forecasting

A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

Spatiotemporal dynamic graph convolutional network for traffic speed forecasting