DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
arXiv:2606.07695v1 Announce Type: new Abstract: Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities. Despite significant recent strides in spatio-temporal modeling, existing approaches often fail to explicitly model the coupling relationships between different modality variables. Accurate MoSTF is challenging, as it requires modeling (1) temporal dynamic heterogeneity under exogenous influences and (2) heterogeneous spatial dependencies alongside complex cross-variable couplings. To address these challen
The continuous growth of urban populations and the increasing complexity of transportation networks necessitate more sophisticated forecasting models to manage traffic and infrastructure efficiently.
Improved spatio-temporal forecasting in urban transportation can lead to significant advancements in smart city infrastructure, reducing congestion, optimizing resource allocation, and increasing safety.
This research introduces a novel deep learning framework that explicitly models the complex interconnectedness of diverse traffic modalities, moving beyond traditional siloed forecasting approaches.
- · Smart city developers
- · Urban planners
- · Logistics companies
- · AI/ML researchers in transportation
- · Cities with inefficient traffic management
- · Companies relying on outdated forecasting models
More accurate traffic predictions will enable optimized routing, dynamic public transport scheduling, and real-time incident management.
Reduced urban congestion could lead to economic benefits through improved productivity and environmental benefits from lower emissions.
The demonstrated dual-domain learning for spatio-temporal dynamics could inspire similar advancements in other complex interconnected systems, such as energy grids or supply chains.
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