Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting. (arXiv:2211.00641v1 [cs.LG])

In this technical report, we present our solutions to the Traffic4cast 2022
core challenge and extended challenge. In this competition, the participants
are required to predict the traffic states for the future 15-minute based on
the vehicle counter data in the previous hour. Compared to other competitions
in the same series, this year focuses on the prediction of different data
sources and sparse vertex-to-edge generalization. To address these issues, we
introduce the Transposed Variational Auto-encoder (TVAE) model to reconstruct
the missing data and Graph Attention Networks (GAT) to strengthen the
correlations between learned representations. We further apply feature
selection to learn traffic patterns from diverse but easily available data. Our
solutions have ranked first in both challenges on the final leaderboard. The
source code is available at url{}



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