Traffic Congestion Signal Inference Based on Trajectory Correlation: A Trajectory Similarity-Based Graph Convolutional Network

dc.contributor.author	Gu, Xiaoning
dc.contributor.author	Chen, Chao
dc.contributor.author	Feng, Rui
dc.contributor.author	Shao, Mengru
dc.contributor.author	Yao, Baozhen
dc.date.accessioned	2026-08-20T20:54:28Z
dc.date.available	2026-08-20T20:54:28Z
dc.date.copyright	2025/08/08
dc.date.issued	2025
dc.identifier.other	JTEPBS.TEENG-9071.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4313653
dc.description.abstract	AbstractUrban traffic flow often exhibits significant dynamic spatiotemporal correlations, leading to uncertain and complex traffic states. Accurately forecasting congestion propagation, which reflects the network dynamics of the congestion scenarios, is ...
dc.publisher	American Society of Civil Engineers
dc.title	Traffic Congestion Signal Inference Based on Trajectory Correlation: A Trajectory Similarity-Based Graph Convolutional Network
dc.type	Journal Article
dc.journal.volume	151
dc.journal.issue	10
dc.journal.title	Journal of Transportation Engineering, Part A: Systems
dc.identifier.doi	10.1061/JTEPBS.TEENG-9071
dc.journal.fristpage	04025078-1
dc.journal.lastpage	04025078-15
dc.page	15
dc.tree	Journal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 010
dc.contenttype	Fulltext