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In recent years, graph neural networks (GNNs) combined with variants ofrecurrent neural networks (RNNs) have reached state-of-the-art performance inspatiotemporal forecasting tasks. This is particularly the case for trafficforecasting, where GNN models use the graph structure of road networks toaccount for spatial correlation between links and nodes. Recent solutions areeither based on complex graph operations or avoiding predefined graphs. Thispaper proposes a new sequence-to-sequence architecture to extract thespatiotemporal correlation at multiple levels of abstraction using GNN-RNNcells with sparse architecture to decrease training time compared to morecomplex designs. Encoding the same input sequence through multiple encoders,with an incremental increase in encoder layers, enables the network to learngeneral and detailed information through multilevel abstraction. We furtherpresent a new benchmark dataset of street-level segment traffic data fromMontreal, Canada. Unlike highways, urban road segments are cyclic andcharacterized by complicated spatial dependencies. Experimental results on theMETR-LA benchmark highway and our MSLTD street-level segment datasetsdemonstrate that our model improves performance by more than 7% for one-hourprediction compared to the baseline methods while reducing computing resourcerequirements by more than half compared to other competing methods.

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction