Predicting traffic propagation flow in urban road network with multi-graph convolutional network

• Published in: Complex & intelligent systems Vol. 10; no. 1; pp. 23 - 35
• Main Authors: Yang, Haiqiang, Li, Zihan, Qi, Yashuai
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2024; Springer Nature B.V; Springer
• Subjects: Artificial neural networks; Communications traffic; Complexity; Computational Intelligence; Data Structures and Information Theory; Engineering; Graph convolutional network; Multilayers; Original Article; Propagation; Roads & highways; Spatial data; Spatial–temporal features; Traffic flow; Traffic models; Traffic prediction; Traffic propagation flow; Traffic volume; Upstream; Traffic prediction; Graph convolutional network; Spatial–temporal features; Traffic propagation flow

Traffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme. Recent works successfully used graph convolutional network (GCN) and specific time-series model to forecast traffic flow by capturing the spatial–temporal...