# General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting

arXiv: https://arxiv.org/abs/2608.17440 · HTML: https://mlsanity.com/paper/2608.17440/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Builds on (most cited first, 22 of 22)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [Spatial-Temporal Transformer Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2001.02908/) (2020)
- [Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting](https://mlsanity.com/paper/2206.09112/) (2022)
- [LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2306.08259/) (2023)
- [GRAG: Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2405.16506/) (2024)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [DGL-KE: Training Knowledge Graph Embeddings at Scale](https://mlsanity.com/paper/2004.08532/) (2020)
- [Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods](https://mlsanity.com/paper/2106.09078/) (2021)
- [Spatio-Temporal Data Mining: A Survey of Problems and Methods](https://mlsanity.com/paper/1711.04710/) (2017)
- [KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2011.14992/) (2020)
- [Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learning](https://mlsanity.com/paper/2408.12116/) (2024)
- [Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting](https://mlsanity.com/paper/2306.15927/) (2023)
- [SUSTeR: Sparse Unstructured Spatio Temporal Reconstruction on Traffic Prediction](https://mlsanity.com/paper/2502.16935/) (2025)
- [Small Graph Is All You Need: DeepStateGNN for Scalable Traffic Forecasting](https://mlsanity.com/paper/2502.14525/) (2025)
- [Fusing Cross-Domain Knowledge from Multimodal Data to Solve Problems in the Physical World](https://mlsanity.com/paper/2506.03155/) (2025)
- [Context-aware knowledge graph framework for traffic speed forecasting using graph neural network](https://mlsanity.com/paper/2407.17703/) (2024)

## Abstract (arXiv)

Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity or road-network topology. This paper presents a spatio-temporal prediction framework, developed to incorporate knowledge in various forms. This framework aims to improve sensor-level, contextual understanding of the environment. A general-purpose knowledge graph (e.g., Wikidata) is used to create semantic subgraphs around traffic sensors and generate knowledge graph embeddings that capture meaningful relationships, such as nearby points of interest, administrative hierarchies, and the functional roles of locations. These embeddings are then fused with conventional traffic sensor graphs to provide additional adjacency matrices informed by semantics. This allows GNNs to learn the semantic context beyond physical connectivity. This study differs from previous research in two key ways. Firstly, rather than proposing a novel GNN architecture, it demonstrates the general impact of external knowledge on prediction accuracy. Secondly, experiments with well-established traffic forecasting approaches show that external knowledge provides additional information that street network data alone cannot convey. The results show that integrating data from general-purpose knowledge graphs and sensor networks through data fusion can enhance the prediction accuracy of traffic forecasting models, and offers a potential pathway toward improved interpretability.