Zero-shot cross-city ride-hailing demand prediction leveraging transferable urban spatiotemporal dynamics

Accurate ride-hailing demand prediction is crucial for platform operational efficiency and transportation management. However, in emerging markets where historical trip data are entirely unavailable, conventional data-driven models as well as transfer learning approaches become infeasible, which creates a critical barrier for platform expansion and public-sector regulation. To bridge this gap, this study proposes a novel two-stage framework for zero-shot (i.e., target-data-free) cross-city ride-hailing demand prediction, in which total demand prediction is decoupled from its spatiotemporal allocation. In the first stage, a linear regression model estimates city-level aggregate demand using universally available macroeconomic indicators and built-environment features. In the second stage, a neural network learns transferable POI temporal activity patterns to allocate this demand across space and time, serving as a proxy for urban mobility rhythms without requiring target-city trip records. The framework is validated on a dataset of 5 million ride-hailing trips from 10 cities under a strict zero-shot experimental design. Results show that the method achieves superior spatial transferability and significantly outperforms baseline models, particularly when transferring knowledge from large, data-rich cities to smaller, data-scarce ones. The predicted demand accurately captures key spatiotemporal dynamics—such as morning and evening peaks—and the learned POI activity profiles are highly interpretable, aligning with known urban activity schedules. This study contributes significantly to both research and practice. Methodologically, it presents a robust and transferable solution for demand forecasting under complete data scarcity. Experimental results demonstrate that the proposed method reduces RMSE by up to 64.55% and MAPE by up to 66.31% compared to baseline models, particularly when transferring knowledge from data-rich metropolises to data-scarce emerging cities. For platform operators, it provides an actionable tool for strategic market entry, fleet sizing, and risk assessment. For transportation planners and policymakers, it enables proactive evaluation of traffic impacts, coordination with public transit, and evidence-based regulatory design in emerging mobility markets.