Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner

Understand

Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system.

• Existing methods aim to reconstruct STTD using low-dimensional models.
• However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations.
• Here, we present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation.