Transferring Traffic Predictions to Urban Regions Without Target Data

The scarcity of spatiotemporal traffic data for many urban regions significantly limits the availability of location-specific predictive models for traffic management, mobility services, and road safety. For example, whereas some cities release taxi data for several years, for many other cities traffic data is not available. Although existing transfer learning approaches aim to alleviate this problem, they require at least some data to be available in the target urban region. Traffic prediction in the regions without data remains a critical and unsolved task. To this extent, we propose Zero-ST, a novel transfer learning approach that leverages inductive road features and road topology to learn latent road representations encoding traffic pattern similarity. Our experimental evaluation on real-world data demonstrates the effectiveness of Zero-ST for the traffic speed prediction task in several urban regions. On this task, Zero-ST achieves an error reduction of at least 4.77% concerning MAE compared to the baselines.