Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory
Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory
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DOI：
10.1109/tits.2023.3243087
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发表时间：
2023-05
影响因子：
8.5
通讯作者：
Zhiwen Zhang;Hongjun Wang;Z. Fan;Xuan Song;R. Shibasaki
中科院分区：
文献类型：
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作者：
Zhiwen Zhang;Hongjun Wang;Z. Fan;Xuan Song;R. Shibasaki
如何从城市GPS轨迹生成道路路况是交通系统中的一个重要问题。然而，由于观测数据的不确定性或众包系统的报告量有限，这种计算过程往往会出现严重的缺失值问题。传统的张量分解方法通过协同过滤的方式学习时空依赖性，忽略了复杂的路网结构信息和时间异质性。在这项研究中，我们提出了一个多视角模型与多方面的先验知识，估算交通状态计算从现实世界的轨迹数据集。具体而言，在空间视图中，我们考虑了道路网络的异质性，并对相邻道路段的多重关系进行建模，而不是关注特定类型的道路段。同时，时间模式也被看作是一个异质的图形结构，区分每周/每小时的时间视图相邻。最后，我们融合上述时空特征，以提供在不同稀疏条件下的鲁棒估计。对两类失踪情况（即，随机和非随机）表明，所提出的插补方法优于所有其他最先进的方法。此外，我们的模型表示可解释的模式，时空图分析。
How to generate road conditions from urban GPS trajectory is an important problem in transportation systems. However, this computation process usually suffers from serious missing value problem due to the observation uncertainty or limited reports from crowdsourcing systems. Conventional tensor factorization approaches learn the spatio-temporal dependencies in a collaborative filtering way, which ignores the complex road network structure information and temporal heterogeneity. In this study, we propose a multi-view model with multiple aspects of prior knowledge to impute traffic state computed from a real-world trajectory dataset. More specifically, in the spatial view, rather than focusing on a specific type of road segment, we take the heterogeneity of road network into consideration and model the multiple relations of adjacent road segments. Meanwhile, the temporal pattern is also viewed as a heterogeneous graphical structure that discriminates the weekly/hourly adjacency in the temporal view. Finally, we fuse the above spatio-temporal features to provide a robust estimation under different sparse conditions. Intensive experiments on two types of missing scenarios (i.e., random and non-random) demonstrate that the proposed imputation method outperforms all the other state-of-the-art approaches. In addition, our model represents interpretable patterns for spatio-temporal graph analysis.