  Multistep traffic forecasting on road networks is a crucial task in
successful intelligent transportation system applications. To capture the
complex non-stationary temporal dynamics and spatial dependency in multistep
traffic-condition prediction, we propose a novel deep learning framework named
attention graph convolutional sequence-to-sequence model (AGC-Seq2Seq). In the
proposed deep learning framework, spatial and temporal dependencies are modeled
through the Seq2Seq model and graph convolution network separately, and the
attention mechanism along with a newly designed training method based on the
Seq2Seq architecture is proposed to overcome the difficulty in multistep
prediction and further capture the temporal heterogeneity of traffic pattern.
We conduct numerical tests to compare AGC-Seq2Seq with other benchmark models
using a real-world dataset. The results indicate that our model yields the best
prediction performance in terms of various prediction error measures.
Furthermore, the variation of spatiotemporal correlation of traffic conditions
under different perdition steps and road segments is revealed through
sensitivity analyses.
