  To capture spatial relationships and temporal dynamics in traffic data,
spatio-temporal models for traffic forecasting have drawn significant attention
in recent years. Most of the recent works employed graph neural networks(GNN)
with multiple layers to capture the spatial dependency. However, road junctions
with different hop-distance can carry distinct traffic information which should
be exploited separately but existing multi-layer GNNs are incompetent to
discriminate between their impact. Again, to capture the temporal
interrelationship, recurrent neural networks are common in state-of-the-art
approaches that often fail to capture long-range dependencies. Furthermore,
traffic data shows repeated patterns in a daily or weekly period which should
be addressed explicitly. To address these limitations, we have designed a
Simplified Spatio-temporal Traffic forecasting GNN(SST-GNN) that effectively
encodes the spatial dependency by separately aggregating different neighborhood
representations rather than with multiple layers and capture the temporal
dependency with a simple yet effective weighted spatio-temporal aggregation
mechanism. We capture the periodic traffic patterns by using a novel position
encoding scheme with historical and current data in two different models. With
extensive experimental analysis, we have shown that our model has significantly
outperformed the state-of-the-art models on three real-world traffic datasets
from the Performance Measurement System (PeMS).
