Grid Partition-Based Dynamic Spatial–Temporal Graph Convolutional Network for Large-Scale Traffic Flow Forecasting

Grid Partition-Based Dynamic Spatial–Temporal Graph Convolutional Network for Large-Scale Traffic Flow Forecasting

Accurate forecasting of city-level large-scale traffic flow is crucial for efficient traffic management and effective transport planning. However, previously proposed traffic flow prediction methods model dynamic spatial correlations across entire traffic networks, leading to high computational comp...

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Bibliographic Details
Main Authors: Lifeng Gao, Liujia Chen, Agen Qiu, Qinglian Wang, Jianlong Wang, Cai Chen, Fuhao Zhang, Geli Ou’er
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:ISPRS International Journal of Geo-Information
Subjects:
graph convolutional network
spatiotemporal forecasting
large-scale
global-local spatial dependency
traffic flow
Online Access:https://www.mdpi.com/2220-9964/14/5/207
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