# Navigating Spatio-Temporal Heterogeneity: A Graph Transformer Approach for Traffic Forecasting

arXiv: https://arxiv.org/abs/2408.10822 · HTML: https://mlsanity.com/paper/2408.10822/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [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)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Spatial-Temporal Transformer Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2001.02908/) (2020)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction](https://mlsanity.com/paper/1803.01254/) (2018)
- [STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting](https://mlsanity.com/paper/1905.10069/) (2019)
- [TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts](https://mlsanity.com/paper/2403.02600/) (2024)
- [Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation](https://mlsanity.com/paper/2306.10683/) (2023)
- [A Visual Analytics System for Improving Attention-based Traffic Forecasting Models](https://mlsanity.com/paper/2208.04350/) (2022)

## Built on by (most cited first, 3 of 3)

- [Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models](https://mlsanity.com/paper/2601.12083/) (2026)
- [Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction](https://mlsanity.com/paper/2511.07649/) (2025)
- [DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling](https://mlsanity.com/paper/2607.22565/) (2026)

## Abstract (arXiv)

Traffic forecasting has emerged as a crucial research area in the development of smart cities. Although various neural networks with intricate architectures have been developed to address this problem, they still face two key challenges: i) Recent advancements in network designs for modeling spatio-temporal correlations are starting to see diminishing returns in performance enhancements. ii) Additionally, most models do not account for the spatio-temporal heterogeneity inherent in traffic data, i.e., traffic distribution varies significantly across different regions and traffic flow patterns fluctuate across various time slots. To tackle these challenges, we introduce the Spatio-Temporal Graph Transformer (STGormer), which effectively integrates attribute and structure information inherent in traffic data for learning spatio-temporal correlations, and a mixture-of-experts module for capturing heterogeneity along spaital and temporal axes. Specifically, we design two straightforward yet effective spatial encoding methods based on the graph structure and integrate time position encoding into the vanilla transformer to capture spatio-temporal traffic patterns. Additionally, a mixture-of-experts enhanced feedforward neural network (FNN) module adaptively assigns suitable expert layers to distinct patterns via a spatio-temporal gating network, further improving overall prediction accuracy. Experiments on real-world traffic datasets demonstrate that STGormer achieves state-of-the-art performance.