Enabling Dynamic Mobility Observatories Through Open Data, AI, and Digital Twin Technologies: A Case Study of Luxembourg

We address the significant opportunities and inherent challenges in developing advanced mobility observatories, critical tools for managing the profound transformation of urban mobility underway, driven by data proliferation, advances in AI and digital twin technologies. To inform this discussion, we first critically review the landscape of data collection methods - from traditional sources such as travel surveys and traffic counters to emerging streams such as mobile phone and social media data - and highlight the benefits and limitations of each approach. Existing mobility dashboards and observatories are examined to understand their current utility and limitations. Building on this analysis, we present a dynamic observatory architecture proposed for Luxembourg that uses automated Extract, Load, Transform (ELT) pipelines and integrates various open data sources. This experience highlights significant data quality challenges and necessitates mitigation strategies, which are discussed. Crucially, our proposed architecture and the Luxembourg case study highlight the essential role and need for the development of interoperable Local Digital Twins (LDTs). We conclude by advocating that to realise the full potential of next-generation mobility observatories, integrated data spaces and sophisticated AI-driven tools must be adopted for future urban mobility management.