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cs.LG2026
Continuous Cross-Domain Traffic State Prediction via Memory-Augmented Graph Liquid Time-Constant Networks
Jinrong Xiang, Ming Xu
Traffic state prediction is a fundamental task in intelligent transportation systems. In practical applications, some regions suffer from limited traffic observations due to insuff…
cs.LG2026★ 1 cited
Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration
Ming Xu, Jinrong Xiang, Zilong Xie +1
Existing learning-to-rank methods for road networks often fail to incorporate origin-destination (OD) flows and route information, limiting their ability to model long-range spatia…
cs.LG2024
TraffNet: Learning Causality of Traffic Generation for What-if Prediction
Ming Xu, Qiang Ai, Ruimin Li +4
Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control. Although current deep learning methods demonstrate significant ad…