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}

翻译：在本技术报告中,我们提出了解决2022年交通流量问题的核心挑战和扩大挑战的解决方案,在这一竞争中,参与者需要根据前一小时的车辆反数据预测未来15分钟的交通状况。与同一系列的其他竞争相比,今年的重点是预测不同的数据来源和零星的顶点到边缘的概括。为了解决这些问题,我们引入了转换的变换自动编码模型(TVAE),以重建缺失的数据和图形关注网络(TVAE),以加强所了解的演示之间的关联性。我们进一步应用地物选择,从多种但容易获得的数据中学习交通模式。我们的解决办法在最后的首选板上排第一。源代码可在\url{https://github.com/Daftstone/Traffic4cast}查阅。