论文标题
在2021年Neurips上的流量4-暂时和空间的几声转移学习中的地理空间过程
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
论文作者
论文摘要
在2019年和2020年NEURIPS上的IARAI Traffic4Cast竞赛表明，神经网络可以成功地预测未来1小时的未来交通状况，仅在时间和太空垃圾箱中汇总的GPS探测数据。因此，我们重新解释了预测流量条件作为电影完成任务的挑战。 U-Nets被证明是获胜的体系结构，表明在这个复杂的现实世界地理空间过程中提取相关功能的能力。在以前的比赛中，2021年Clufmit4cast 2021现在重点介绍了跨时间和空间的模型鲁棒性和概括性问题。从一个城市转变为完全不同的城市，或者从兴前的时期到世界上的时代，因此引入了明显的领域变化。因此，我们首次发布了具有此类域移动的数据。现在，竞争涵盖了2年的十个城市，提供了从10^12个GPS探测数据中汇编的数据。赢得解决方案可以很好地捕获流量动态，甚至可以应对这些复杂的域移动。令人惊讶的是，这似乎仅需要以前的1H流量动态历史记录和静态道路图作为输入。
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the challenge of forecasting traffic conditions as a movie completion task. U-Nets proved to be the winning architecture, demonstrating an ability to extract relevant features in this complex real-world geo-spatial process. Building on the previous competitions, Traffic4cast 2021 now focuses on the question of model robustness and generalizability across time and space. Moving from one city to an entirely different city, or moving from pre-COVID times to times after COVID hit the world thus introduces a clear domain shift. We thus, for the first time, release data featuring such domain shifts. The competition now covers ten cities over 2 years, providing data compiled from over 10^12 GPS probe data. Winning solutions captured traffic dynamics sufficiently well to even cope with these complex domain shifts. Surprisingly, this seemed to require only the previous 1h traffic dynamic history and static road graph as input.
