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<p>Ultra-wideband (UWB) radios enable decimeter-level positioning. This is posited to unlock unprecedented sophistication and detail in indoor mobility analytics. However, to date industry and academia have resorted to primitive analyses based only on raw trajectories, unable to ascertain <i>where</i> individuals stop and <i>for how long</i>, crucial in many domains. Among these is the museum providing the real-world context and dataset for this paper. We deployed a UWB localization system in a <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(40\,{\times }\,15\)</EquationSource></InlineEquation>&#xa0;<InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\text{m}^{2}\)</EquationSource></InlineEquation> area containing 42 exhibits and track more than 1500 visitors over a 3&#xa0;months period. We confirm that commonplace analyses relying on UWB trajectories alone offer practical insights, yet cannot capture the two key dimensions above. Instead, we exploit state-of-the-art techniques to extract higher-level semantic trajectories directly capturing visitor behavior, and distill a multitude of detailed, multi-layered, actionable insights. Our experience concretely highlights the untapped, disruptive potential of UWB-based mobility analytics—and a way to seize it. To foster further research, in museums and beyond, we publicly release our large UWB dataset (more than 9&#xa0;million positions) along with the one resulting from our higher-level analyses.</p>

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Unleashing the Power of UWB for Indoor Mobility Analytics: A Museum Case Study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月27日

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  • Davide Vecchia,
  • Fatima Hachem,
  • Davide Molteni,
  • Maria Luisa Damiani,
  • Gian Pietro Picco

摘要

Ultra-wideband (UWB) radios enable decimeter-level positioning. This is posited to unlock unprecedented sophistication and detail in indoor mobility analytics. However, to date industry and academia have resorted to primitive analyses based only on raw trajectories, unable to ascertain where individuals stop and for how long, crucial in many domains. Among these is the museum providing the real-world context and dataset for this paper. We deployed a UWB localization system in a \(40\,{\times }\,15\) \(\text{m}^{2}\) area containing 42 exhibits and track more than 1500 visitors over a 3 months period. We confirm that commonplace analyses relying on UWB trajectories alone offer practical insights, yet cannot capture the two key dimensions above. Instead, we exploit state-of-the-art techniques to extract higher-level semantic trajectories directly capturing visitor behavior, and distill a multitude of detailed, multi-layered, actionable insights. Our experience concretely highlights the untapped, disruptive potential of UWB-based mobility analytics—and a way to seize it. To foster further research, in museums and beyond, we publicly release our large UWB dataset (more than 9 million positions) along with the one resulting from our higher-level analyses.