Functional ANOVA Modelling of Pedestrian Counts on Streets in Three European Cities

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.

Enhancing (Publications on) Data Quality: Deeper Data Minding and Fuller Data Confession

“…However, the advances of digital technology and digital media are pushing us to do more, or at least to apply what we already have developed to newer areas and help others to do the same. The study by Bolin et al (2021) on pedestrian counts on streets in Amsterdam, London and Stockholm was made possible by tracing Wi‐Fi signals of unique mobile phones at both street crossings (of a selected location segment). Whereas this is clearly a viable approach, I cannot help wondering about the relationship between the pedestrian counts, which are the stated estimand, and the traced Wi‐Fi signal counts, which are the estimators.…”