GeoAI-Based Few-Shot Transfer Learning for Cross-City Pedestrian Level-of-Service Mapping Using Spatio-Temporal Graph Models

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Abstract
Urban planners need continuous, scalable methods to evaluate pedestrian Level of Service (LOS). Static and locally calibrated approaches fail to capture the dynamic, network-wide, and context-dependent nature of pedestrian activity. While traditional LOS uses fixed density thresholds and data-driven models predict continuous flows, neither supports cross-city analysis due to context-specific assumptions. This study introduces a transferable analytical framework for predicting pedestrian LOS using large scale urban sensor data that captures both recurrent temporal demand patterns and spatial dependencies within street networks. The framework is evaluated using pedestrian sensor data from three cities Melbourne, Dublin, and Zurich, which represent diverse geometries, demand profiles, and sensing infrastructures. Results show strong in-domain Melbourne performance (accuracy 79.7%; Acc±1 99.1%) and effective cross-city generalization. Few-shot fine-tuning with only 5% labeled target-city data recovers 95–99% of in-domain performance, demonstrating practical scalability. KernelSHAP explainability reveals short-term temporal lag features universally dominate predictions, while spatial/contextual factors exhibit city-specific influence tied to local morphology. These findings demonstrate transferable GeoAI methods can support real-time pedestrian congestion monitoring and evidence-based public-space management, offering planners a scalable decision-support tool to enhance walkability, safety, and equitable access to high-quality public spaces in contemporary cities.

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