Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory

Zhang, Zhiwen1; Wang, Hongjun2; Fan, Zipei1; Song, Xuan2; Shibasaki, Ryosuke1

2023-02-01

10.1109/TITS.2023.3243087

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS   Impact Factor & Quartile

1524-9050

1558-0016

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.

Roads Trajectory Tensors Global Positioning System Task analysis Computational modeling Uncertainty Taxi GPS trajectory missing value imputation heterogeneous graph multi-view

SCI

English

Others

Japan Society for the Promotion of Science (JSPS)[22H03573]

Engineering ; Transportation

Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology

WOS:000936247300001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

ENGINEERING

Web of Science

1.Univ Tokyo, Ctr Spatial Informat Sci, Kashiwa, Chiba 2778561, Japan
2.Southern Univ Sci & Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China

Zhang, Zhiwen,Wang, Hongjun,Fan, Zipei,et al. Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2023,PP(99):1-15.

Zhang, Zhiwen,Wang, Hongjun,Fan, Zipei,Song, Xuan,&Shibasaki, Ryosuke.(2023).Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,PP(99),1-15.

Zhang, Zhiwen,et al."Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS PP.99(2023):1-15.