用于估计到达时间的道路网络度量学习
Road Network Metric Learning for Estimated Time of Arrival
摘要：
近年来，深度学习在估计到达时间（Estimated Time of Arrival，ETA）方面取得了可喜的成果，ETA被认为是预测沿给定路径从起点到目的地的行程时间，其中一个关键技术是使用嵌入向量来表示道路网络的元素，如路段（路段）。然而，嵌入存在数据稀疏性问题，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只有很少的浮动汽车经过。数据不足会使嵌入向量处于欠拟合状态，影响ETA预测的准确性。为了解决数据稀疏性问题，我们提出了道路网络度量学习框架ETA（RNML-ETA）。它由两部分组成：（1）预测行进时间的主回归任务，以及（2）改进链路嵌入向量的质量的辅助度量学习任务。我们进一步提出了三角损失，一个新的损失函数，以提高度量学习的效率。我们在大规模真实世界数据集上验证了RNML-ETA的有效性，表明我们的方法优于最先进的模型，并且推广集中在数据较少的冷链接上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
