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{https://github.com/Daftstone/Traffic4cast}

该研究介绍了解决 Traffic4cast 2022 核心挑战和扩展挑战的方法，使用 Transposed Variational Auto-encoder（TVAE）模型重建丢失的数据、Graph Attention Networks（GAT）加强学习表示之间的关联，并进一步采用特征选择从多样但易于获取的数据中学习交通模式，最终在两个挑战中均名列第一。

基于内在特征学习的转置变分自编码器用于交通预测