# FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks

arXiv: https://arxiv.org/abs/2306.08277 · HTML: https://mlsanity.com/paper/2306.08277/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 20 of 20)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Long Range Graph Benchmark](https://mlsanity.com/paper/2206.08164/) (2022)
- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [Position-aware Graph Neural Networks](https://mlsanity.com/paper/1906.04817/) (2019)
- [Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting](https://mlsanity.com/paper/2105.04100/) (2021)
- [GraphReach: Position-Aware Graph Neural Network using Reachability Estimations](https://mlsanity.com/paper/2008.09657/) (2020)
- [Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting](https://mlsanity.com/paper/2109.05225/) (2021)
- [Batching and Matching for Food Delivery in Dynamic Road Networks](https://mlsanity.com/paper/2008.12905/) (2020)
- [Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases](https://mlsanity.com/paper/2203.03965/) (2022)
- [Trajectory Aware Macro-cell Planning for Mobile Users](https://mlsanity.com/paper/1501.02918/) (2015)
- [NetClus: A Scalable Framework for Locating Top-K Sites for Placement of Trajectory-Aware Services](https://mlsanity.com/paper/1702.02809/) (2017)
- [Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics](https://mlsanity.com/paper/2208.08878/) (2022)

## Built on by (most cited first, 14 of 14)

- [Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting](https://mlsanity.com/paper/2405.10800/) (2024)
- [Mirage: Model-Agnostic Graph Distillation for Graph Classification](https://mlsanity.com/paper/2310.09486/) (2023)
- [FlexiReg: Flexible Urban Region Representation Learning](https://mlsanity.com/paper/2503.09128/) (2025)
- [GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking](https://mlsanity.com/paper/2310.01794/) (2023)
- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting](https://mlsanity.com/paper/2402.00397/) (2024)
- [Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid](https://mlsanity.com/paper/2409.00604/) (2024)
- [Frequency Enhanced Pre-training for Cross-city Few-shot Traffic Forecasting](https://mlsanity.com/paper/2406.02614/) (2024)
- [Graph Construction with Flexible Nodes for Traffic Demand Prediction](https://mlsanity.com/paper/2403.00276/) (2024)
- [Bonsai: Gradient-free Graph Condensation for Node Classification](https://mlsanity.com/paper/2410.17579/) (2024)
- [Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence](https://mlsanity.com/paper/2605.18893/) (2026)
- [Spatio-Temporal Partial Sensing Forecast for Long-term Traffic](https://mlsanity.com/paper/2408.02689/) (2024)
- [Spatio-temporal Multivariate Time Series Forecast with Chosen Variables](https://mlsanity.com/paper/2510.24027/) (2025)
- [GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond](https://mlsanity.com/paper/2606.03232/) (2026)

## Abstract (arXiv)

Modelling spatio-temporal processes on road networks is a task of growing importance. While significant progress has been made on developing spatio-temporal graph neural networks (Gnns), existing works are built upon three assumptions that are not practical on real-world road networks. First, they assume sensing on every node of a road network. In reality, due to budget-constraints or sensor failures, all locations (nodes) may not be equipped with sensors. Second, they assume that sensing history is available at all installed sensors. This is unrealistic as well due to sensor failures, loss of packets during communication, etc. Finally, there is an assumption of static road networks. Connectivity within networks change due to road closures, constructions of new roads, etc. In this work, we develop FRIGATE to address all these shortcomings. FRIGATE is powered by a spatio-temporal Gnn that integrates positional, topological, and temporal information into rich inductive node representations. The joint fusion of this diverse information is made feasible through a novel combination of gated Lipschitz embeddings with Lstms. We prove that the proposed Gnn architecture is provably more expressive than message-passing Gnns used in state-of-the-art algorithms. The higher expressivity of FRIGATE naturally translates to superior empirical performance conducted on real-world network-constrained traffic data. In addition, FRIGATE is robust to frugal sensor deployment, changes in road network connectivity, and temporal irregularity in sensing.