论文标题
道路网络度量学习，用于估计到达时间
Road Network Metric Learning for Estimated Time of Arrival
论文作者
论文摘要
最近，深度学习在估计的到达时间（ETA）中取得了令人鼓舞的结果，这被认为可以预测从给定路径沿着目的地到目的地的旅行时间。关键技术之一是使用嵌入向量来表示道路网络的要素，例如链接（路段）。但是，嵌入遇到了数据稀疏问题，即使在Uber和Didi等大型乘车平台中，道路网络中的许多链接也会被太少的浮动汽车所经历。数据不足使嵌入向量处于拟合不足的状态，这破坏了ETA预测的准确性。为了解决数据稀疏问题，我们提出了ETA（RNML-ETA）的道路网络度量学习框架。它由两个组成部分组成：（1）预测旅行时间的主要回归任务，以及（2）辅助度量学习任务，以提高链接嵌入向量的质量。我们进一步提出了三角损失，这是提高公制学习效率的新型损失功能。我们通过表明我们的方法优于最先进的模型，而促销集中在很少的数据上，我们验证了RNML-ETA对大规模实际数据集的有效性。
Recently, deep learning have achieved promising results in Estimated Time of Arrival (ETA), which is considered as predicting the travel time from the origin to the destination along a given path. One of the key techniques is to use embedding vectors to represent the elements of road network, such as the links (road segments). However, the embedding suffers from the data sparsity problem that many links in the road network are traversed by too few floating cars even in large ride-hailing platforms like Uber and DiDi. Insufficient data makes the embedding vectors in an under-fitting status, which undermines the accuracy of ETA prediction. To address the data sparsity problem, we propose the Road Network Metric Learning framework for ETA (RNML-ETA). It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors. We further propose the triangle loss, a novel loss function to improve the efficiency of metric learning. We validated the effectiveness of RNML-ETA on large scale real-world datasets, by showing that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.
