  $\textbf{This is the conference version of our paper: Spatiotemporal Implicit
Neural Representation as a Generalized Traffic Data Learner}$. 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 employed to directly map
coordinates to traffic variables. To unravel the entangled spatial-temporal
interactions, the variability is decomposed into separate processes. We further
enable modeling in irregular spaces such as sensor graphs using spectral
embedding. Through continuous representations, our approach enables the
modeling of a variety of STTD with a unified input, thereby serving as a
generalized learner of the underlying traffic dynamics. It is also shown that
it can learn implicit low-rank priors and smoothness regularization from the
data, making it versatile for learning different dominating data patterns. We
validate its effectiveness through extensive experiments in real-world
scenarios, showcasing applications from corridor to network scales. Empirical
results not only indicate that our model has significant superiority over
conventional low-rank models, but also highlight that the versatility of the
approach. We anticipate that this pioneering modeling perspective could lay the
foundation for universal representation of STTD in various real-world tasks.
$\textbf{The full version can be found at:}$
https://doi.org/10.48550/arXiv.2405.03185.
