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).

本文提出了一种称为简化空时交通预测 GNN 的模型，它通过对不同邻域进行分别聚合的方式有效地编码了空间依赖性，并使用简单而有效的加权空时聚合机制捕获时间依赖性，而且使用了一种新颖的位置编码方案来捕获周期性的交通模式，实验表明该模型优于最先进的交通预测模型。