Statistical modelling of pedestrian flows

Abstract

Pedestrian counts and in particular their relation to the buildings in the vicinity of the street and to the structure of the street network is of central interest in the space syntax field. This report is concerned with using statistical techniques to model pedestrian counts and in particular how these counts vary over the day. Of interest is whether the variation over the day for a street can be predicted based on its density type, describing the nearby buildings, and street type, describing its role in in the city’s overall street network. Using data from Amsterdam, London and Stockholm the hour-by-hour pedestrian counts are modelled with the so-called functional ANOVA method, using the aforementioned types to divide the streets into groups. Additionally, the effect of the presence of schools, stores and public transport stops near the streets on pedestrian counts is considered. The model is fitted in a Bayesian framework using the integrated nested Laplace approximation technique. The results indicate that this model works well but that it might be somewhat too rigid to capture all the variability in the data, failing to capture some of the difference between groups and between the cities. Some possible extensions to the model to remedy this are suggested.