Robust and Hierarchical Spatial Relation Analysis for Traffic Forecasting

doi:10.1109/TITS.2022.3217054

Robust and Hierarchical Spatial Relation Analysis for Traffic Forecasting

2023-01-01

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DOI：10.1109/TITS.2022.3217054

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How to model the complex spatial-temporal relation in traffic data is an important problem for precisely predicting the future status of a city traffic system. Existing traffic forecasting methods rarely consider the traffic state trend, and the robust spatial relation has not been well explored. To tackle these issues, we design a novel Robust And Hierarchical spatial Relation Analysis (RAHRA) method to calculate the local-period spatial relation, which applies temporal context information in both traffic state and trend similarities. This could capture abundant traffic patterns and learn stable and comprehensive spatial relations for accurate traffic forecasting. Furthermore, we introduce a Temporal Attention Module (TAM) to capture the temporal features and propose a Future Feature Inference Module (FFIM) to infer the future traffic information. Experiments on four real-world traffic datasets demonstrate that the proposed method outperforms the other state-of-the-art methods.

Keywords:

Market research

Feature extraction

Forecasting

Time series analysis

Transportation

Deep learning

Convolutional neural networks

Traffic forecasting

traffic state trend

spatial relation

temporal convolution network

Robust and Hierarchical Spatial Relation Analysis for Traffic Forecasting

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