An attention-based deep learning model for citywide traffic flow forecasting

Abstract

Prompt and accurate traffic flow forecasting is a key foundation of urban traffic management. However, the flows in different areas and feature channels (inflow/outflow) may correspond to different degrees of importance in forecasting flows. Many forecasting models inadequately consider this heterogeneity, resulting in decreased predictive accuracy. To overcome this problem, an attention-based hybrid spatiotemporal residual model assisted by spatial and channel information is proposed in this study. By assigning different weights (attention levels) to different regions, the spatial attention module selects relatively important locations from all inputs in the modeling process. Similarly, the channel attention module selects relatively important channels from the multichannel feature map in the modeling process by assigning different weights. The proposed model provides effective selection and attention results for key areas and channels, respectively, during the forecasting process, thereby decreasing the computational overhead and increasing the accuracy. In the case involving Beijing, the proposed model exhibits a 3.7% lower prediction error, and its runtime is 60.9% less the model without attention, indicating that the spatial and channel attention modules are instrumental in increasing the forecasting efficiency. Moreover, in the case involving Shanghai, the proposed model outperforms other models in terms of generalizability and practicality.

Keywords

• attention mechanism
• long short-term memory model
• residual network
• spatiotemporal forecasting
• traffic flow