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<p>As the world rapidly urbanises and cities become larger and more complex, understanding pedestrian dynamics is paramount. New data sources, particularly those that measure pedestrian counts (i.e. ‘footfall’), offer potential as a means of better understanding the fundamental spatio-temporal structures that characterise aggregate pedestrian behaviour. However, footfall data are often complex and influenced by a wide range of social, spatial and temporal factors, which complicates interpretation. This paper applies principal component analysis (PCA) to hourly pedestrian count data from Melbourne, Australia, to extract the key temporal signatures that underpin observed urban footfall patterns. PCA can reduce the dimensionality of noisy pedestrian flow data, revealing dominant activity patterns such as weekday commuting cycles and weekend leisure activities. By subsequently analysing pedestrian volumes through the lens of these components, we start to expose the underlying types of pedestrian activities that characterise different neighbourhoods. In addition, we can distinguish multiple overlapping activity patterns within a single location, identifying changes in urban functionality and detecting shifts in mobility trends. The impacts of external shocks, such as the COVID-19 pandemic, are particularly stark. These findings shed light on the intricacies of urban mobility and suggest that there is value in the use of PCA as a means to better understand urban dynamics.</p>

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Leveraging principal component analysis to uncover urban pedestrian dynamics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月10日

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  • Jack Liddle,
  • Wenhua Jiang,
  • Nick Malleson

摘要

As the world rapidly urbanises and cities become larger and more complex, understanding pedestrian dynamics is paramount. New data sources, particularly those that measure pedestrian counts (i.e. ‘footfall’), offer potential as a means of better understanding the fundamental spatio-temporal structures that characterise aggregate pedestrian behaviour. However, footfall data are often complex and influenced by a wide range of social, spatial and temporal factors, which complicates interpretation. This paper applies principal component analysis (PCA) to hourly pedestrian count data from Melbourne, Australia, to extract the key temporal signatures that underpin observed urban footfall patterns. PCA can reduce the dimensionality of noisy pedestrian flow data, revealing dominant activity patterns such as weekday commuting cycles and weekend leisure activities. By subsequently analysing pedestrian volumes through the lens of these components, we start to expose the underlying types of pedestrian activities that characterise different neighbourhoods. In addition, we can distinguish multiple overlapping activity patterns within a single location, identifying changes in urban functionality and detecting shifts in mobility trends. The impacts of external shocks, such as the COVID-19 pandemic, are particularly stark. These findings shed light on the intricacies of urban mobility and suggest that there is value in the use of PCA as a means to better understand urban dynamics.