Functional ANOVA Modelling of Pedestrian Counts on Streets in Three European Cities
利用阿姆斯特丹、伦敦和斯德哥尔摩的逐时行人计数数据，提出一个贝叶斯函数ANOVA模型，解释街道行人流量如何随建筑密度和街道类型变化，并与四种机器学习方法比较预测能力。
Abstract 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.