Advances in traffic forecasting technology can greatly impact urban mobility. In the traffic4cast competition, the task of short-term traffic prediction is tackled in unprecedented detail, with traffic volume and speed information available at 5 minute intervals and high spatial resolution. To improve generalization to unknown cities, as required in the 2021 extended challenge, we propose to predict small quadratic city sections, rather than processing a full-city-raster at once. At test time, breaking down the test data into spatially-cropped overlapping snippets improves stability and robustness of the final predictions, since multiple patches covering one cell can be processed independently. With the performance on the traffic4cast test data and further experiments on a validation set it is shown that patch-wise prediction indeed improves accuracy. Further advantages can be gained with a Unet++ architecture and with an increasing number of patches per sample processed at test time. We conclude that our snippet-based method, combined with other successful network architectures proposed in the competition, can leverage performance, in particular on unseen cities. All source code is available at https://github.com/NinaWie/NeurIPS2021-traffic4cast.
翻译:交通预测技术的进步可以极大地影响城市的流动性。在交通流量4播送的竞争中,短期交通预测的任务是以前所未有的细节,以5分钟的间隔和高空间分辨率提供交通量和速度信息。为了按照2021年长期挑战的要求改进对未知城市的概括化,我们提议预测小型四边城区,而不是同时处理全城市的全光栅。测试时,将测试数据分解成空间覆盖的重叠片段可以提高最终预测的稳定性和稳健性,因为覆盖一个细胞的多个补丁可以独立处理。随着流量4播送测试数据的性能和对验证数据集的进一步实验,显示对准的预测确实提高了准确性。通过Unet+++架构和测试时处理的每个样本中越来越多的补丁可以获得更多的优势。我们的结论是,我们基于片段的方法,加上竞争中提议的其他成功的网络架构,可以提高性能,特别是在看不见城市。所有源码都可在 https://github.com/NinaWie/NeurIPS2021-trastast.