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

Abstract

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.