SOURCE-LINKED INTELLIGENCE
Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimation
The estimation of Annual Average Daily Traffic (AADT) is vital for transportation planning and infrastructure maintenance, yet obtaining accurate values for an entire urban network across multiple years remains challenging due to the high cost and spatial sparsity of physical sensors. This research proposes a novel spatio-temporally complementary feature propagation framework that leverages the strengths of two distinct data sources: spatially sparse but temporally dense loop detector data, and a spatially complete but temporally sparse macroscopic transportation model. The methodology highlig
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T14:47:04.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.