SOURCE-LINKED INTELLIGENCE
Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting
Traffic flow forecasting is essential to intelligent transportation systems. Large-scale traffic forecasting requires jointly modeling local spatial dependencies and cross-region context.Spatial dependencies between geographically neighboring nodes are heterogeneous due to differences in road identity and travel direction, while acquiring global information through allpairs node interactions incurs substantial computational costs. Therefore, capturing local heterogeneity while efficiently acquiring long-range context remains an important challenge in largescale traffic forecasting. To address
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-23T10:00:28.000Z
First collected: 2026-09-24T01:22:21.678Z. This is not the publication date.