HMIAN: a Hierarchical Mapping and Interactive Attention Data Fusion Network for Traffic Forecasting

Abstract

With the development of intelligent transportation system (ITS), the vital technology of ITS, short-term traffic forecasting, gains increasing attention. However, the existing prediction models ignore the impact of urban functional zones on traffic data, resulting in inaccurate extractions of dynamic spatial relationships from network. Furthermore, how to calculate the influence of external factors such as weather and holidays on traffic is an unsolved problem. This paper proposes a spatio-temporal hierarchical mapping and interactive attention network (HMIAN), which extracts the spatial features from traffic network by constructing functional zones, and designs an effective external factors fusion method. HMIAN uses the hierarchical mapping structure to aggregate the roads into functional zones, calculate the interaction between functional zones and feed this information back to the spatial features. And the interactive attention mechanism is utilized to fuse the traffic data with external factors effectively, and extracts temporal features. In addition, some experiments were carried out on three real traffic data sets. First, experiment results show that the proposed model better prediction performance compared with other existing approaches in more complex traffic network. Second, the longitudinal comparison experiment verifies that the hierarchical mapping structure is effective in extracting spatial features in complex road network. Finally, the influence of different external factors and fusion methods on traffic prediction are compared, which provides a consult for subsequent research on the influence of external factors.