Paper Info
Title
Graph Attention LSTM: A Spatiotemporal Approach for Traffic Flow Forecasting
Abstract
This article investigates a traffic-flow forecasting problem based on long–short term memory (LSTM), an artificial recurrent neural network architecture used in deep learning. By representing the road network as an unweighted directed graph, the traffic flow prediction problem becomes how to capture the spatiotemporal dependencies among nodes in the graph. We present a novel graph-attention LSTM s...
Year
DOI
Venue
2022
10.1109/MITS.2020.2990165
IEEE Intelligent Transportation Systems Magazine
Keywords
DocType
Volume
Data models,Forecasting,Spatiotemporal phenomena,Roads,Predictive models,Computer architecture,Sensors
Journal
14
Issue
ISSN
Citations 
2
1939-1390
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Tianqi Zhang16821.52
Ge Guo272749.03