arXiv Machine Learning September 25, 2026 By Linghang Sun, Qishen Zhou, Michail A. Makridis, Anastasios Kouvelas

Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimation

Read the original on arXiv Machine Learning →

The paper introduces a spatio-temporally complementary feature propagation framework for estimating Annual Average Daily Traffic (AADT) across an entire urban network. It combines spatially sparse but temporally dense loop detector data with spatially complete but temporally sparse macroscopic transportation models, using a Poisson energy minimization algorithm on directed graphs with flow ratio matrices. Tested in Zurich, the method converges quickly and achieves a normalized mean absolute error below 10%, demonstrating its effectiveness and scalability.

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