Due to the rapid development of Internet of Things (IoT) technologies, many
online web apps (e.g., Google Map and Uber) estimate the travel time of
trajectory data collected by mobile devices. However, in reality, complex
factors, such as network communication and energy constraints, make multiple
trajectories collected at a low sampling rate. In this case, this paper aims to
resolve the problem of travel time estimation (TTE) and route recovery in
sparse scenarios, which often leads to the uncertain label of travel time and
route between continuously sampled GPS points. We formulate this problem as an
inexact supervision problem in which the training data has coarsely grained
labels and jointly solve the tasks of TTE and route recovery. And we argue that
both two tasks are complementary to each other in the model-learning procedure
and hold such a relation: more precise travel time can lead to better inference
for routes, in turn, resulting in a more accurate time estimation). Based on
this assumption, we propose an EM algorithm to alternatively estimate the
travel time of inferred route through weak supervision in E step and retrieve
the route based on estimated travel time in M step for sparse trajectories. We
conducted experiments on three real-world trajectory datasets and demonstrated
the effectiveness of the proposed method.

该论文针对 IoT 技术下数据采样不足的情况，提出了一种基于 EM 算法的不完全监督模型，通过粗略标记数据，同时解决旅行时间估计和路径恢复问题，实现更高精度的路径推断和更准确的时间预测。

从稀疏轨迹估计行程时间和路径

Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories

Graph neural networks (GNNs) achieve remarkable success in graph-based
semi-supervised node classification, leveraging the information from
neighboring nodes to improve the representation learning of target node. The
success of GNNs at node classification depends on the assumption that connected
nodes tend to have the same label. However, such an assumption does not always
work, limiting the performance of GNNs at node classification. In this paper,
we propose label-consistency based graph neural network(LC-GNN), leveraging
node pairs unconnected but with the same labels to enlarge the receptive field
of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed
LC-GNN outperforms traditional GNNs in graph-based semi-supervised node
classification.We further show the superiority of LC-GNN in sparse scenarios
with only a handful of labeled nodes.

本文提出了基于标签一致性的图神经网络（LC-GNN），扩大了 GNN 中的节点感受野，利用未连接但标签相同的节点对。在基准数据集上的实验证明，LC-GNN 在图形半监督节点分类方面优于传统的 GNN，在只有少数标记节点的稀疏场景中也表现出优越性。