# Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting

arXiv: https://arxiv.org/abs/2302.09956 · HTML: https://mlsanity.com/paper/2302.09956/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 72 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [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)
- [GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs](https://mlsanity.com/paper/1803.07294/) (2018)
- [Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution](https://mlsanity.com/paper/2104.14917/) (2021)
- [Mish: A Self Regularized Non-Monotonic Activation Function](https://mlsanity.com/paper/1908.08681/) (2019)
- [Spectral Feature Augmentation for Graph Contrastive Learning and Beyond](https://mlsanity.com/paper/2212.01026/) (2022)
- [MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction](https://mlsanity.com/paper/2110.01401/) (2021)
- [Generative Adversarial Networks for Spatio-temporal Data: A Survey](https://mlsanity.com/paper/2008.08903/) (2020)
- [Tensor Representations for Action Recognition](https://mlsanity.com/paper/2012.14371/) (2020)
- [Contrastive Laplacian Eigenmaps](https://mlsanity.com/paper/2201.05493/) (2022)
- [Graph Markov Network for Traffic Forecasting with Missing Data](https://mlsanity.com/paper/1912.05457/) (2019)
- [Graph-adaptive Rectified Linear Unit for Graph Neural Networks](https://mlsanity.com/paper/2202.06281/) (2022)
- [Incrementally Improving Graph WaveNet Performance on Traffic Prediction](https://mlsanity.com/paper/1912.07390/) (2019)
- [TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting](https://mlsanity.com/paper/2011.05554/) (2020)
- [ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed](https://mlsanity.com/paper/1911.13181/) (2019)
- [COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference](https://mlsanity.com/paper/1909.11048/) (2019)
- [Flight Delay Prediction using Airport Situational Awareness Map](https://mlsanity.com/paper/1911.01605/) (2019)
- [Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map](https://mlsanity.com/paper/2105.08969/) (2021)
- [On the Inclusion of Spatial Information for Spatio-Temporal Neural Networks](https://mlsanity.com/paper/2007.07559/) (2020)

## Built on by (most cited first, 9 of 9)

- [Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey](https://mlsanity.com/paper/2503.13502/) (2025)
- [Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)](https://mlsanity.com/paper/2305.05237/) (2023)
- [Pre-training with Random Orthogonal Projection Image Modeling](https://mlsanity.com/paper/2310.18737/) (2023)
- [Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting](https://mlsanity.com/paper/2305.06587/) (2023)
- [BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics](https://mlsanity.com/paper/2406.08990/) (2024)
- [Continually learning out-of-distribution spatiotemporal data for robust energy forecasting](https://mlsanity.com/paper/2306.06385/) (2023)
- [Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning](https://mlsanity.com/paper/2309.04296/) (2023)
- [Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned](https://mlsanity.com/paper/2406.12709/) (2024)
- [Evaluating Spatio-Temporal Forecasting Trade-offs Between Graph Neural Networks and Foundation Models](https://mlsanity.com/paper/2511.05179/) (2025)

## Abstract (arXiv)

Traffic forecasting is a critical task to extract values from cyber-physical infrastructures, which is the backbone of smart transportation. However owing to external contexts, the dynamics at each sensor are unique. For example, the afternoon peaks at sensors near schools are more likely to occur earlier than those near residential areas. In this paper, we first analyze real-world traffic data to show that each sensor has a unique dynamic. Further analysis also shows that each pair of sensors also has a unique dynamic. Then, we explore how node embedding learns the unique dynamics at every sensor location. Next, we propose a novel module called Spatial Graph Transformers (SGT) where we use node embedding to leverage the self-attention mechanism to ensure that the information flow between two sensors is adaptive with respect to the unique dynamic of each pair. Finally, we present Graph Self-attention WaveNet (G-SWaN) to address the complex, non-linear spatiotemporal traffic dynamics. Through empirical experiments on four real-world, open datasets, we show that the proposed method achieves superior performance on both traffic speed and flow forecasting. Code is available at: https://github.com/aprbw/G-SWaN