Generalizable Implicit Neural Representation As a Universal Spatiotemporal Traffic Data Learner

TL;DR

This paper introduces a universal implicit neural representation framework for modeling complex spatiotemporal traffic data, capable of capturing diverse dynamics and irregular spatial structures across different scales.

Contribution

It proposes a novel neural implicit representation approach that unifies various traffic data patterns and spatial structures, surpassing traditional low-dimensional models.

Findings

Outperforms conventional low-rank models in real-world scenarios

Effectively models irregular sensor graph spaces using spectral embedding

Demonstrates versatility across corridor and network scale applications

Abstract

$\textbf{This is the conference version of our paper: Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner}$. 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.