SDCN: Sparsity and Diversity Driven Correlation Networks for Traffic Demand Forecasting

Traffic demand forecasting is essential to intelligent transportation systems and is widely used to support urban planning, traffic management and vehicle dispatching. One challenge of this problem is to model the complex spatial-temporal correlation. Although both factors have been studied, many of the existing works have strong limitations. They rely too heavily on the locality assumption (i.e., the local area is more relevant than the remote area) and only use a distance-based correlation measurement. However, the spatial correlation is also global (i.e., areas far away may also be relevant) and sparse. And it’s insufficient to measure the spatial correlation using only the distance measurement. In this paper, a sparsity and diversity driven correlation network is proposed to tackle these issues. Firstly, Multiple sparse correlation graphs are carefully generated to encode sparsity and diversity. Then a newly designed hybrid graph filtering module (HGFM) leverages them to learn a more expressive node representation. Finally, the HGFM-based recurrent filtering module (RFM) is introduced to handle the spatial-temporal correlation. Extensive experiments conducted on real-world datasets demonstrate the competitiveness of our model while showing the significance of sparsity and diversity.