论文标题
TLAB：基于HR-NET的交通图电影预测
TLab: Traffic Map Movie Forecasting Based on HR-NET
论文作者
论文摘要
大规模时空交通数据的有效预测问题长期以来一直困扰着智能运输领域的研究人员。受数据量的限制，很少实现全市交通状态的预测。因此，无法真正理解整个城市的复杂城市运输系统。得益于IARAI等组织的努力，他们提供的大量开放数据使研究成为可能。在2020年的竞争解决方案中，我们进一步设计了基于HR-NET和UNET的多种变体。通过功能工程，手工制作的功能以通道形式输入到模型中。值得注意的是，要了解地理位置的固有属性，我们提出了一种名为Geo-ebedding的新颖方法，这有助于显着提高模型的准确性。此外，我们探讨了激活功能和优化器选择的影响，以及模型训练期间的技巧。就预测准确性而言，我们的解决方案在2020年Neurips 2020，流量4cast Challenge中获得了第二名。
The problem of the effective prediction for large-scale spatio-temporal traffic data has long haunted researchers in the field of intelligent transportation. Limited by the quantity of data, citywide traffic state prediction was seldom achieved. Hence the complex urban transportation system of an entire city cannot be truly understood. Thanks to the efforts of organizations like IARAI, the massive open data provided by them has made the research possible. In our 2020 Competition solution, we further design multiple variants based on HR-NET and UNet. Through feature engineering, the hand-crafted features are input into the model in a form of channels. It is worth noting that, to learn the inherent attributes of geographical locations, we proposed a novel method called geo-embedding, which contributes to significant improvement in the accuracy of the model. In addition, we explored the influence of the selection of activation functions and optimizers, as well as tricks during model training on the model performance. In terms of prediction accuracy, our solution has won 2nd place in NeurIPS 2020, Traffic4cast Challenge.
