Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner

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.

Accepted at TR-Part C

References in corpus (26)

• Neural Tangent Kernel: Convergence and Generalization in Neural Networks
• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
• Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks
• A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion
• A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation
• A simple contagion process describes spreading of traffic jams in urban networks
• Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
• UrbanFM: Inferring Fine-Grained Urban Flows
• Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
• ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation
• The low-rank hypothesis of complex systems
• Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
• Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control
• Real-time forecasting of metro origin-destination matrices with high-order weighted dynamic mode decomposition
• Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach
• Recovery of Future Data via Convolution Nuclear Norm Minimization
• Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
• Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis
• Channel-Aware Low-Rank Adaptation in Time Series Forecasting
• Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection
• HyperTime: Implicit Neural Representation for Time Series
• Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks
• Generalised Implicit Neural Representations
• Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
• High-Dimensional Fault Tolerance Testing of Highly Automated Vehicles Based on Low-Rank Models

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