论文标题
转移的变异自动编码器，具有固有功能学习的流量预测
Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting
论文作者
论文摘要
在这份技术报告中，我们向Traffic4cast 2022核心挑战和扩展挑战提供了解决方案。在这场比赛中，要求参与者根据上一小时的车辆计数器数据预测未来15分钟的交通状态。与同一系列中的其他竞赛相比，今年的重点是预测不同的数据源和稀疏的顶点到边缘的概括。为了解决这些问题，我们介绍了转换的变分自动编码器（TVAE）模型，以重建缺失的数据和图形注意力网络（GAT），以增强学到的表示形式之间的相关性。我们进一步应用功能选择以从不同但易于获得的数据中学习流量模式。 我们的解决方案在最终排行榜上的两个挑战中都排名第一。源代码可在\ url {https://github.com/daftstone/traffic4cast}中获得
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}
