Multi-Agent Foundation Models for Urban Mobility : The Malmö Elderly Case
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Understanding travel behaviour is crucial for developing inclusive, adaptable transportation systems in ageing cities. Yet many public authorities lack the expertise and budget for advanced analytics. We present a modular, multi-agent framework that employs Large Language Models (LLMs) to lower the entry barrier to data-driven mobility analysis. The pipeline extracts interpretable, policy-oriented insights from unstructured survey data and outputs structured reports without manual coding. We demonstrate the approach using Malmö's 2023 travel survey for residents aged 65+, and benchmark its recommendations against those of (i) a single-agent LLM service (Single Agent), representative of current commercial offerings, and (ii) a human expert baseline (Human), both evaluated in a blinded expert review. The system achieves superior reasoning quality compared to the single-agent baseline, while performing slightly lower on interpretability and focus, and approaches expert-level quality overall - at a fraction of the cost and effort. Key constraints are the current lack of real-time data ingestion and dependence on proprietary commercial APIs. The study provides an open-source proof of concept showing how multi-agent LLMs can make urban mobility analytics more accessible, transparent, and timely. All code and evaluation materials are publicly available.