Functional ANOVA modelling of pedestrian counts on streets in three European cities
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ArticleAuthors
Bolin, David
Verendel, Vilhelm
Berghauser Pont, Meta
Stavroulaki, Ioanna
Ivarsson, Oscar
Håkansson, Erik
Date
2021-01-01Embargo End Date
2022-01-01Submitted Date
2019-11-30Permanent link to this record
http://hdl.handle.net/10754/666955
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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.Citation
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Blackwell Publishing LtdAdditional Links
https://onlinelibrary.wiley.com/doi/10.1111/rssa.12646ae974a485f413a2113503eed53cd6c53
10.1111/rssa.12646