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.

通过参数化空时交通数据（STTD）为隐式神经表示，我们提供了一种新的方法来解决 STTD 学习问题。通过在低维度范围内识别潜在动力学和分解空时交互作用，我们的方法能够进行各种 STTD 的建模，并展示了其在实际应用中的有效性。

时空隐式神经表示作为广义交通数据学习器

Spatiotemporal Implicit Neural Representation as a Generalized Traffic  Data Learner

Sparsity and missing data problems are very common in spatiotemporal traffic
data collected from various sensing systems. Making accurate imputation is
critical to many applications in intelligent transportation systems. In this
paper, we formulate the missing data imputation problem in spatiotemporal
traffic data in a low-rank tensor completion (LRTC) framework and define a
novel truncated nuclear norm (TNN) on traffic tensors of
location$\times$day$\times$time of day. In particular, we introduce an
universal rate parameter to control the degree of truncation on all tensor
modes in the proposed LRTC-TNN model, and this allows us to better characterize
the hidden patterns in spatiotemporal traffic data. Based on the framework of
the Alternating Direction Method of Multipliers (ADMM), we present an efficient
algorithm to obtain the optimal solution for each variable. We conduct
numerical experiments on four spatiotemporal traffic data sets, and our results
show that the proposed LRTC-TNN model outperforms many state-of-the-art
imputation models with missing rates/patterns. Moreover, the proposed model
also outperforms other baseline models in extreme missing scenarios.

本文提出了一种用于解决空间时间交通数据中缺失数据问题的低秩张量完成方法，并定义了一个新的截断核范数，利用交替方向乘子法（ADMM）的框架提出了一种有效的算法来获得每个变量的最优解。数值实验表明，所提出的方法优于许多最先进的缺失数据模型。