The relation between pedestrian flows, the structure of the city and the street network is of central interest in urban research. However, studies of this have traditionally been based on small data sets and simplistic statistical methods. Because of a recent large-scale cross-country pedestrian survey, there is now enough data available to study this in greater detail than before, using modern statistical methods. We propose a functional ANOVA model to explain how the pedestrian flow for a street varies over the day based on its density type, describing the nearby buildings, and street type, describing its role in the city’s overall street network. The model is formulated and estimated in a Bayesian framework using hour-by-hour pedestrian counts from the three European cities, Amsterdam, London and Stockholm. To assess the predictive power of the model, which could be of interest when building new neighbourhoods, it is compared with four common methods from machine learning, including neural networks and random forests. The results indicate that this model works well but that there is room for improvement in capturing the variability in the data, especially between cities.

中文翻译：

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三个欧洲城市街道行人数量的功能方差分析模型

行人流量、城市结构和街道网络之间的关系是城市研究的核心兴趣所在。然而，这方面的研究传统上是基于小数据集和简单的统计方法。由于最近的大规模越野行人调查，现在有足够的数据可以使用现代统计方法比以前更详细地研究这一点。我们提出了一个功能方差分析模型，以解释街道的行人流量如何根据其密度类型在一天中变化，描述附近的建筑物，以及街道类型，描述其在城市整体街道网络中的作用。该模型是在贝叶斯框架中使用来自三个欧洲城市阿姆斯特丹、伦敦和斯德哥尔摩的每小时行人计数来制定和估计的。为了评估模型的预测能力（这在构建新社区时可能会引起人们的兴趣），我们将其与机器学习中的四种常用方法（包括神经网络和随机森林）进行了比较。结果表明，该模型运行良好，但在捕捉数据的可变性方面仍有改进的余地，尤其是城市之间。