论文标题
采取良好的U-NET进行交通预测
Towards Good Practices of U-Net for Traffic Forecasting
论文作者
论文摘要
该技术报告为2020年流量4cast挑战提供了解决方案。我们将流量预测问题视为未来的框架预测任务，其时间依赖性相对较弱（可能是由于随机城市交通动态）和强大的先验知识，\ textit {i.e。}，城市的路线图。由于这些原因，我们将U-NET用作骨干模型，并提出了一种路线图生成方法，以使预测的交通流更加理性。同时，我们使用基于验证集的微调策略来防止过度拟合，从而有效地改善了预测结果。在本报告的结尾，我们进一步讨论了我们在以后的工作中考虑或可以探讨的几种方法：（1）利用固有的数据模式，例如季节性； （2）在不同城市之间蒸馏和转移常识。我们还分析了评估度量的有效性。
This technical report presents a solution for the 2020 Traffic4Cast Challenge. We consider the traffic forecasting problem as a future frame prediction task with relatively weak temporal dependencies (might be due to stochastic urban traffic dynamics) and strong prior knowledge, \textit{i.e.}, the roadmaps of the cities. For these reasons, we use the U-Net as the backbone model, and we propose a roadmap generation method to make the predicted traffic flows more rational. Meanwhile, we use a fine-tuning strategy based on the validation set to prevent overfitting, which effectively improves the prediction results. At the end of this report, we further discuss several approaches that we have considered or could be explored in future work: (1) harnessing inherent data patterns, such as seasonality; (2) distilling and transferring common knowledge between different cities. We also analyze the validity of the evaluation metric.
