  Traffic prediction is a spatiotemporal predictive task that plays an
essential role in intelligent transportation systems. Today, graph
convolutional neural networks (GCNNs) have become the prevailing models in the
traffic prediction literature since they excel at extracting spatial
correlations. In this work, we classify the components of successful GCNN
prediction models and analyze the effects of matrix factorization, attention
mechanism, and weight sharing on their performance. Furthermore, we compare
these variations against random forests, a traditional regression method that
predates GCNNs by over 15 years. We evaluated these methods using simulated
data of two regions in Toronto as well as real-world sensor data from selected
California highways. We found that incorporating matrix factorization,
attention, and location-specific model weights either individually or
collectively into GCNNs can result in a better overall performance. Moreover,
although random forest regression is a less compact model, it matches or
exceeds the performance of all variations of GCNNs in our experiments. This
suggests that the current graph convolutional methods may not be the best
approach to traffic prediction and there is still room for improvement.
Finally, our findings also suggest that for future research on GCNN for traffic
prediction to be credible, researchers must include performance comparison to
random forests.
