Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner

TL;DR

This paper introduces a novel implicit neural representation framework for modeling spatiotemporal traffic data, enabling unified, flexible, and high-fidelity learning of complex traffic dynamics across various scales and data types.

Contribution

It proposes a generalized neural implicit model for STTD that captures high-frequency structures, disentangles spatial-temporal interactions, and adapts to irregular spaces, surpassing traditional low-rank models.

Findings

Outperforms conventional low-rank models in real-world scenarios.

Demonstrates versatility across different data domains and network topologies.

Provides insights into the inductive biases of traffic data modeling.

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 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 extends to different data domains, output resolutions, and network topologies. Comprehensive model analyses provide further insight into the inductive bias of STTD. We anticipate that this pioneering modeling perspective could lay the foundation for universal representation of STTD in various real-world tasks. Code is available at https://github.com/tongnie/traffic_dynamics.