KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting (2011.14992v2)

Abstract: While considering the spatial and temporal features of traffic, capturing the impacts of various external factors on travel is an essential step towards achieving accurate traffic forecasting. However, existing studies seldom consider external factors or neglect the effect of the complex correlations among external factors on traffic. Intuitively, knowledge graphs can naturally describe these correlations. Since knowledge graphs and traffic networks are essentially heterogeneous networks, it is challenging to integrate the information in both networks. On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks. We first construct a knowledge graph for traffic forecasting and derive knowledge representations by a knowledge representation learning method named KR-EAR. Then, we propose the Knowledge Fusion Cell (KF-Cell) to combine the knowledge and traffic features as the input of a spatial-temporal graph convolutional backbone network. Experimental results on the real-world dataset show that our strategy enhances the forecasting performances of backbones at various prediction horizons. The ablation and perturbation analysis further verify the effectiveness and robustness of the proposed method. To the best of our knowledge, this is the first study that constructs and utilizes a knowledge graph to facilitate traffic forecasting; it also offers a promising direction to integrate external information and spatial-temporal information for traffic forecasting. The source code is available at https://github.com/lehaifeng/T-GCN/tree/master/KST-GCN.

• The paper introduces a novel framework (KST-GCN) that fuses knowledge graphs with spatial-temporal data to improve traffic forecasting accuracy.
• It employs the KR-EAR model and a dedicated Knowledge Fusion Cell (KF-Cell) to integrate external influences like weather and POIs with dynamic traffic data.
• Experimental evaluations on Shenzhen datasets demonstrate significant improvements over traditional models across multiple prediction horizons.

Overview of KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

The paper introduces the KST-GCN, a sophisticated framework for enhancing traffic forecasting by integrating spatial-temporal graph convolutional networks with a knowledge-driven approach. The authors address the often-overlooked influence of external factors, such as weather and points of interest (POIs), on traffic dynamics.

Motivation and Background

Traditionally, traffic forecasting models have focused primarily on capturing spatial and temporal dependencies. However, they frequently neglect the complex interplay between these dependencies and various external influences. To bridge this gap, the authors propose a method that uses knowledge graphs to capture and incorporate such external factors effectively.

Methodology

Knowledge Graph Construction: The method begins by constructing a knowledge graph unique to traffic forecasting. This graph encodes the relationships and correlations between traffic information and external factors. By employing the KR-EAR knowledge representation learning model, the graph is transformed into embeddings that capture these complex semantic interactions.

KF-Cell Integration: At the core of the KST-GCN is the Knowledge Fusion Cell (KF-Cell), which plays a pivotal role in blending these knowledge representations with traffic features. The KF-Cell serves as a bridge, enabling the integration of external knowledge into the spatial-temporal graph convolutional network. This is achieved by processing both static and dynamic factors, thus enhancing the backbone's ability to forecast traffic more accurately.

Spatial-Temporal Graph Convolutional Networks: By utilizing graph neural networks, specifically GCNs combined with recurrent neural networks like GRU, the KST-GCN effectively models both the spatial dependencies in road networks and the temporal dynamics of traffic.

Experimental Evaluation

Extensive evaluations on real-world datasets, particularly from Shenzhen, demonstrate that KST-GCN variants—KF-DCRNN and KF-T-GCN—significantly outperform traditional methods and their own backbone models. The paper notes improvements across multiple prediction horizons, highlighting the utility of integrating external factor knowledge.

Ablation and Perturbation Studies: The authors conducted thorough ablation studies to verify the impact of incorporating specific types of knowledge, such as POIs and weather. Additionally, robustness checks using perturbation analysis confirmed the model's resilience to data noise, further validating its practical applicability.

Implications and Future Directions

The implications of this paper are significant both practically and theoretically. By more accurately forecasting traffic, urban planning and management can be vastly improved, reducing congestion and enhancing transportation efficiency. Theoretically, this work opens avenues for further integration of heterogeneous data sources into predictive models, offering a robust framework for future research.

The paper also suggests that while the current approach is promising, the full potential of knowledge-driven spatial-temporal networks could be further realized with more comprehensive datasets, allowing for richer and more nuanced knowledge graphs.

Conclusion

The KST-GCN represents a valuable contribution to the field of intelligent transportation systems, showcasing a novel approach to integrating external data into traffic forecasting models. The methodological innovations and the promising results underscore the potential of combining knowledge representation techniques with advanced neural network architectures in tackling complex prediction tasks.