A Hierarchy-Aware Approach to Cross-Region Spatial-Temporal Inference of Unarchived Event in Urban Mobility Infrastructure

Forecasting the accessibility of urban mobility infrastructure, such as transport networks and charging places for vehicles, is crucial to our daily lives. However, the event records of the mobility infrastructure in a region can be unarchived due to low sensor coverage or different jurisdictions. This hinders accurate assessment and leads to a waste of resources. We target the problem of spatial-temporal inference of events with zero historical and real-time in-region records in urban mobility infrastructures. The difficulties lie in the limited knowledge of events in the target region and the lack of generalizability across different regions and distinct mobility infrastructures. To address the problem, we propose a hierarchy-driven machine-learning approach that exploits the hierarchical transport networks as domain-general features and heterogeneous urban sensory data as domain-specific features to support cross-region inference without directly using historical and real-time event records. We evaluate our approach on eight real-world datasets with two downstream tasks: charging availability and road accessibility. The evaluation results show the efficacy and adaptability of our approach by consistently achieving statistically significant improvement over state-of-the-art methods.