Bridge augments a graph neural network backbone with time-aware retrieval from a memory of region-time windows to improve cold-start and cross-city urban delivery demand forecasting.
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.arXiv preprint arXiv:1709.04875
14 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.
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representative citing papers
STReasoner uses S-GRPO reinforcement learning to let LLMs integrate time series, graphs, and text for spatio-temporal reasoning, delivering 17-135% accuracy gains over baselines on a new four-task benchmark at 0.004X the cost of proprietary models.
Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
AirQualityBench is a realistic global benchmark using hourly data from 3720 stations across 2021-2025 for six pollutants, preserving native missingness masks and evaluating on inverse-transformed physical scales.
SCOT uses Sinkhorn entropic optimal transport to learn explicit soft correspondences between unequal region sets for multi-source cross-city transfer, adding contrastive sharpening and cycle reconstruction for stability and a prototype hub for multi-source alignment.
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
A synchronization-safe dynamic microgrid formation method with constraint-aware spatio-temporal graph convolutional networks accelerates distribution system restoration while enforcing safety constraints.
STM3 is a new multiscale Mamba mixture-of-experts model with graph causal networks and contrastive routing that reports state-of-the-art results on 10 long-term spatio-temporal forecasting benchmarks.
Spectrally unstable nodes are identified via graph-spectral distortion analysis as primary drivers of reliability failures; isolating them yields a stable subgraph for learning with propagation-based recovery for the isolated nodes, improving performance across GNNs and spectral clustering under攻击s.
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
A 3D pattern-matching model using Earth Mover's Distance on conflict data outperforms the VIEWS ensemble benchmark in predicting fatalities.
SocialMirror reconstructs 3D meshes of closely interacting humans from monocular videos using semantic guidance from vision-language models and geometric constraints in a diffusion model to handle occlusions and maintain temporal and spatial consistency.
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.
Extends Social-STGCNN with CVAE for multimodal trajectory prediction and reports moderate gains plus better diversity on ETH/UCY benchmarks and robot data.
citing papers explorer
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Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand
Bridge augments a graph neural network backbone with time-aware retrieval from a memory of region-time windows to improve cold-start and cross-city urban delivery demand forecasting.
-
STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning
STReasoner uses S-GRPO reinforcement learning to let LLMs integrate time series, graphs, and text for spatio-temporal reasoning, delivering 17-135% accuracy gains over baselines on a new four-task benchmark at 0.004X the cost of proprietary models.
-
Graph Retention Networks for Dynamic Graphs
Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
-
AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecasting
AirQualityBench is a realistic global benchmark using hourly data from 3720 stations across 2021-2025 for six pollutants, preserving native missingness masks and evaluating on inverse-transformed physical scales.
-
SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective
SCOT uses Sinkhorn entropic optimal transport to learn explicit soft correspondences between unequal region sets for multi-source cross-city transfer, adding contrastive sharpening and cycle reconstruction for stability and a prototype hub for multi-source alignment.
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GAMMA-Net: Adaptive Long-Horizon Traffic Spatio-Temporal Forecasting Model based on Interleaved Graph Attention and Multi-Axis Mamba
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
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Synchronization-Safe Dynamic Microgrid Formation for DER-Led Distribution System Restoration With Constraint-Aware Graph Learning
A synchronization-safe dynamic microgrid formation method with constraint-aware spatio-temporal graph convolutional networks accelerates distribution system restoration while enforcing safety constraints.
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STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction
STM3 is a new multiscale Mamba mixture-of-experts model with graph causal networks and contrastive routing that reports state-of-the-art results on 10 long-term spatio-temporal forecasting benchmarks.
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Spectrally unstable nodes drive reliability failures in graph learning
Spectrally unstable nodes are identified via graph-spectral distortion analysis as primary drivers of reliability failures; isolating them yields a stable subgraph for learning with propagation-based recovery for the isolated nodes, improving performance across GNNs and spectral clustering under攻击s.
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TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
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The geometry of conflict : 3D Spatio-temporal patterns in fatalities prediction
A 3D pattern-matching model using Earth Mover's Distance on conflict data outperforms the VIEWS ensemble benchmark in predicting fatalities.
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SocialMirror: Reconstructing 3D Human Interaction Behaviors from Monocular Videos with Semantic and Geometric Guidance
SocialMirror reconstructs 3D meshes of closely interacting humans from monocular videos using semantic guidance from vision-language models and geometric constraints in a diffusion model to handle occlusions and maintain temporal and spatial consistency.
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Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.
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On Improving Multimodal Pedestrian Trajectory Prediction with CVAE: A Study on Benchmark and Robot Data
Extends Social-STGCNN with CVAE for multimodal trajectory prediction and reports moderate gains plus better diversity on ETH/UCY benchmarks and robot data.