Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner

arXiv:2405.03185v1 Announce Type: new
Abstract: 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. To discern the underlying dynamics in low-dimensional regimes, coordinate-based neural networks that can encode high-frequency structures are …

abstract aim arxiv cs.lg data dimensions generalized however low novel paradigm patterns representation them traffic transportation type