Graph Neural Network for Traffic Forecasting: A Survey (2101.11174v4)

Abstract: Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems. In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms. We also present a comprehensive list of open data and source resources for each problem and identify future research directions. To the best of our knowledge, this paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems. We have also created a public GitHub repository where the latest papers, open data, and source resources will be updated.

• The paper surveys various GNN methodologies, emphasizing their power in modeling complex spatial-temporal traffic patterns.
• It details how GCNs, GATs, and diffusion models significantly outperform traditional approaches on benchmarks like METR-LA and PeMS.
• The survey outlines challenges and future directions, calling for robust multi-task frameworks and centralized data repositories for traffic forecasts.

Essay: Graph Neural Networks for Traffic Forecasting - A Survey

The surveyed paper presents a comprehensive examination of the application of Graph Neural Networks (GNNs) within the domain of traffic forecasting. The research highlights a substantial compendium of GNN-based approaches that have been developed to model complex traffic patterns, leveraging the inherent graph structures present in transportation systems.

Overview of Research Contributions

The paper primarily focuses on the utilization of GNNs to address traffic forecasting across three primary levels: road-level, region-level, and station-level. Within these classifications, diverse graph structures such as sensor graphs, road segment graphs, and station graphs are employed to represent the spatial intricacies of transportation infrastructure effectively.

The application of GNNs, particularly Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), is emphasized due to their proficiency in capturing non-Euclidean spatial dependencies. These networks have demonstrated marked improvements over traditional machine learning models in forecasting tasks, thanks to their ability to naturally incorporate complex spatial relationships.

Methodological Insights

The paper comprehensively delineates several methodologies adopted in GNN-based traffic forecasting. Spatiotemporal GNNs are categorized into RNN-based approaches and CNN-based methodologies. Convolutional GNNs, especially GCNs and Diffusion Graph Convolutional Networks, dominate due to their scalability and efficacy. Attention mechanisms have also gained traction, providing dynamic adaptability in model architecture.

Furthermore, the paper discusses the formulation of adjacency matrices, a critical component in GNNs that dictates spatial dependencies. Static matrices based on physical connectivity or geographic proximity are contrasted with dynamic matrices that evolve with data changes, highlighting the versatility needed in traffic forecasting applications.

Performance and Data Utilization

Performance benchmarks on prominent datasets like METR-LA and PeMS are meticulously detailed, revealing consistently strong results from GNN-based models. The paper notes that state-of-the-art models such as DCRNN and Graph WaveNet significantly outperform conventional models in standard datasets.

A notable mention is the challenge of data heterogeneity. Robust data pre-processing and graph construction are pivotal, as they directly influence the accuracy and reliability of model predictions. The incorporation of external factors such as weather and event data further enriches model inputs, though it necessitates careful data fusion techniques.

Challenges and Future Prospects

The paper identifies several challenges in the domain, including the need for robust multi-task frameworks, advanced model interpretation mechanisms, and overcoming data quality issues. The necessity for a centralized data repository to unify graph-based traffic datasets is particularly emphasized as vital for advancing comparative research.

Looking forward, the paper encourages the exploration of integrating GNNs with other machine learning paradigms such as transfer learning, meta-learning, and AutoML to address current limitations. These integrations could potentially enhance model adaptability and reduce reliance on specific datasets.

Conclusion

In essence, the paper enriches the discourse on traffic forecasting through GNNs by providing a methodical critique of existing methodologies while outlining potential pathways for future exploration. The survey underscores the promise of GNNs in transforming how spatial-temporal dependencies are harnessed within intelligent transportation systems, offering a clear trajectory for emerging research and practical implementations.