论文标题
流量4cast 2020-图集合网和功能和损失功能设计的重要性对于流量预测
Traffic4cast 2020 -- Graph Ensemble Net and the Importance of Feature And Loss Function Design for Traffic Prediction
论文作者
论文摘要
本文详细介绍了我们针对2020年流量的解决方案。类似于2019年流量4Cast，Traffic4cast 2020挑战其参赛者，以开发可以预测大城市未来交通状态的算法。我们的团队在两个方面解决了这一挑战。我们研究了功能和损失功能设计的重要性，并从去年开始了最佳性能的U-NET解决方案。我们还探索了图神经网络的使用，并引入了一种新颖的集合GNN体系结构，该体系结构优于去年的GNN解决方案。尽管我们的GNN得到了改善，但仍无法匹配U-NET的性能，并且讨论了此短缺的潜在原因。我们的最终解决方案是我们U-NET和GNN的合奏，在2020年流量4中获得了第四名解决方案。
This paper details our solution to Traffic4cast 2020. Similar to Traffic4cast 2019, Traffic4cast 2020 challenged its contestants to develop algorithms that can predict the future traffic states of big cities. Our team tackled this challenge on two fronts. We studied the importance of feature and loss function design, and achieved significant improvement to the best performing U-Net solution from last year. We also explored the use of Graph Neural Networks and introduced a novel ensemble GNN architecture which outperformed the GNN solution from last year. While our GNN was improved, it was still unable to match the performance of U-Nets and the potential reasons for this shortfall were discussed. Our final solution, an ensemble of our U-Net and GNN, achieved the 4th place solution in Traffic4cast 2020.
