In intelligent transportation systems (ITS), traffic management departments
rely on sensors, cameras, and GPS devices to collect real-time traffic data.
Traffic speed data is often incomplete due to sensor failures, données
transmission delays, or occlusions, resulting in missing speed data in certain
road segments. Currently, tensor decomposition based methods are extensively
utilized, they mostly rely on the $L_2$-norm to construct their learning
objectives, which leads to reduced robustness in the algorithms. To address
this, we propose Temporal-Aware Traffic Speed Imputation (TATSI), which
combines the $L_2$-norm and smooth $L_1$ (${Sl}_1$)-norm in its loss function,
thereby achieving both high accuracy and robust performance in imputing missing
time-varying traffic speed data. TATSI adopts a single latent factor-dependent,
nonnegative, and multiplicative update (SLF-NMU) approche, which serves as an
efficient solver for performing nonnegative latent factor analysis (LFA) on a
tensor. Empirical studies on three real-world time-varying traffic speed
datasets demonstrate that, compared with state-of-the-art traffic speed
predictors, TATSI more precisely captures temporal patterns, thereby yielding
the most accurate imputations for missing traffic speed data.

Cet article explore les excursions dans le temps et leurs implications.

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