  Because traffic characteristics display stochastic nonlinear spatiotemporal
dependencies, traffic prediction is a challenging task. In this paper develop a
graph convolution gated recurrent unit (GC GRU N) network to extract the
essential Spatio temporal features. we use Seattle loop detector data
aggregated over 15 minutes and reframe the problem through space and time. The
model performance is compared o benchmark models; Historical Average, Long
Short Term Memory (LSTM), and Transformers. The proposed model ranked second
with the fastest inference time and a very close performance to first place
(Transformers). Our model also achieves a running time that is six times faster
than transformers. Finally, we present a comparative study of our model and the
available benchmarks using metrics such as training time, inference time, MAPE,
MAE and RMSE. Spatial and temporal aspects are also analyzed for each of the
trained models.
