  Accurate traffic flow forecasting is a crucial research topic in
transportation management. However, it is a challenging problem due to rapidly
changing traffic conditions, high nonlinearity of traffic flow, and complex
spatial and temporal correlations of road networks. Most existing studies
either try to capture the spatial dependencies between roads using the same
semantic graph over different time steps, or assume all sensors on the roads
are equally likely to be connected regardless of the distance between them.
However, we observe that the spatial dependencies between roads indeed change
over time, and two distant roads are not likely to be helpful to each other
when predicting the traffic flow, both of which limit the performance of
existing studies. In this paper, we propose Temporal Graph Learning Recurrent
Neural Network (TGLRN) to address these problems. More precisely, to
effectively model the nature of time series, we leverage Recurrent Neural
Networks (RNNs) to dynamically construct a graph at each time step, thereby
capturing the time-evolving spatial dependencies between roads (i.e.,
microscopic view). Simultaneously, we provide the Adaptive Structure
Information to the model, ensuring that close and consecutive sensors are
considered to be more important for predicting the traffic flow (i.e.,
macroscopic view). Furthermore, to endow TGLRN with robustness, we introduce an
edge sampling strategy when constructing the graph at each time step, which
eventually leads to further improvements on the model performance. Experimental
results on four commonly used real-world benchmark datasets show the
effectiveness of TGLRN.
