Functional ANOVA modelling of pedestrian counts on streets in three European cities
Artikel i vetenskaplig tidskrift, 2021

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.

space syntax

count data

Bayesian modelling

pedestrian flows


David Bolin

King Abdullah University of Science and Technology (KAUST)

Vilhelm Verendel

Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd

Meta Berghauser Pont

Chalmers, Arkitektur och samhällsbyggnadsteknik, Stadsbyggnad

Ioanna Stavroulaki

Chalmers, Arkitektur och samhällsbyggnadsteknik, Stadsbyggnad

Oscar Ivarsson

Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd

Erik Håkansson

Göteborgs universitet

Journal of the Royal Statistical Society. Series A, (Statistics in Society)

0964-1998 (ISSN) 1467-985X (eISSN)

Vol. 184 4 1176-1198


Bioinformatik (beräkningsbiologi)

Annan samhällsbyggnadsteknik

Sannolikhetsteori och statistik



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