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In this technical report, we present our solutions to the Traffic4cast 2022core challenge and extended challenge. In this competition, the participantsare required to predict the traffic states for the future 15-minute based onthe vehicle counter data in the previous hour. Compared to other competitionsin the same series, this year focuses on the prediction of different datasources and sparse vertex-to-edge generalization. To address these issues, weintroduce the Transposed Variational Auto-encoder (TVAE) model to reconstructthe missing data and Graph Attention Networks (GAT) to strengthen thecorrelations between learned representations. We further apply featureselection 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{https://github.com/Daftstone/Traffic4cast}

Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting