Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convolutional networks, recurrent networks, and Transformers, achieve strong results on standard benchmarks, but their architectures are designed by hand, requiring significant expert effort and producing models that often generalize poorly across cities and datasets.
arXiv:2607. 26467v1 Announce Type: new Abstract: Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch.
arXiv:2606. 09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS).
arXiv:2606. 14956v1 Announce Type: new Abstract: Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement.
arXiv:2608. 04075v1 Announce Type: cross Abstract: Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both space and time.
arXiv:2603. 11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions.
arXiv:2607. 24885v1 Announce Type: cross Abstract: Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility.
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.
arXiv:2607. 13108v1 Announce Type: cross Abstract: Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting.
arXiv:2606. 21072v2 Announce Type: replace-cross Abstract: Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making.
arXiv:2505. 13102v4 Announce Type: replace-cross Abstract: Unlike conventional "black-box" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions.
arXiv:2510. 03381v3 Announce Type: replace-cross Abstract: Interchanges are crucial nodes for vehicle transfers between highways, yet the lack of real-time ramp detectors creates blind spots in traffic prediction.