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dc.contributor.authorBolin, David
dc.contributor.authorVerendel, Vilhelm
dc.contributor.authorBerghauser Pont, Meta
dc.contributor.authorStavroulaki, Ioanna
dc.contributor.authorIvarsson, Oscar
dc.contributor.authorHåkansson, Erik
dc.date.accessioned2021-01-20T12:40:43Z
dc.date.available2021-01-20T12:40:43Z
dc.date.issued2021-01-01
dc.date.submitted2019-11-30
dc.identifier.citationBolin, D., Verendel, V., Berghauser Pont, M., Stavroulaki, I., Ivarsson, O., & Håkansson, E. (2021). Functional ANOVA modelling of pedestrian counts on streets in three European cities. Journal of the Royal Statistical Society: Series A (Statistics in Society). doi:10.1111/rssa.12646
dc.identifier.issn1467-985X
dc.identifier.issn0964-1998
dc.identifier.doi10.1111/rssa.12646
dc.identifier.urihttp://hdl.handle.net/10754/666955
dc.description.abstractThe 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.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1111/rssa.12646
dc.rightsArchived with thanks to Journal of the Royal Statistical Society. Series A: Statistics in Society
dc.titleFunctional ANOVA modelling of pedestrian counts on streets in three European cities
dc.typeArticle
dc.contributor.departmentKing Abdullah University of Science and Technology, Thuwal, Saudi Arabia
dc.identifier.journalJournal of the Royal Statistical Society. Series A: Statistics in Society
dc.rights.embargodate2022-01-01
dc.eprint.versionPost-print
dc.contributor.institutionChalmers University of Technology, Gothenburg, Sweden
dc.contributor.institutionUniversity of Gothenburg, Gothenburg, Sweden
kaust.personBolin, David
dc.date.accepted2020-11-24
dc.identifier.eid2-s2.0-85099036008


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