Dynamic Interactive Graph Convolutional Recurrent Network for bidirectional spatiotemporal traffic flow forecasting

Accurate prediction of traffic inflow and outflow is essential for efficient urban mobility management and multimodal transit systems. However, existing approaches struggle with two main challenges: (i) The dynamic spatiotemporal heterogeneity that varies across different regions and times, complicating the prediction task. (ii) The asymmetric interdependence between inflows and outflows is often overlooked, leading to an inadequate representation of intricate bidirectional relationships. To address these challenges, we propose the Dynamic Interactive Graph Convolutional Recurrent Network (DIGCRN). In particular, DIGCRN incorporates an inflow and outflow feature interaction learning that utilizes an interactive gated mechanism to achieve spatiotemporal characteristic transformation, thereby capturing the asymmetric interdependence between inflows and outflows. Subsequently, a gated recurrent unit based on an adaptive graph convolutional network is employed to recursively capture spatiotemporal features. The final multi-scale convolution module realizes the fusion of inflow and outflow features at different granularity levels. Comprehensive empirical evaluations on the Hangzhou Metro and New York City Taxi datasets indicate that DIGCRN surpasses all baselines, achieving improvements of up to 3. 46% in mean absolute error (MAE) and 7. 28% in root mean square error (RMSE) compared to the best-performing baseline models. The code is available at https: //github. com/LiuZhen1234567/DIGCRN.

Authors

• Zhen Liu University of Electronic Science and Technology of China Megvii Technology
• Shiqi Zhang Zhejiang University of Technology
• Yuzhuang Pian
• Yonghong Liu

Keywords

• Traffic flow prediction
• Graph convolutional network
• Spatiotemporal forecasting
• Dynamic graph learning