  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.
