• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Functional ANOVA modelling of pedestrian counts on streets in three European cities

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    main.pdf
    Size:
    6.379Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Embargo End Date:
    2022-01-01
    Download
    Type
    Article
    Authors
    Bolin, David cc
    Verendel, Vilhelm
    Berghauser Pont, Meta
    Stavroulaki, Ioanna
    Ivarsson, Oscar
    Håkansson, Erik
    KAUST Department
    King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
    Date
    2021-01-01
    Embargo End Date
    2022-01-01
    Submitted Date
    2019-11-30
    Permanent link to this record
    http://hdl.handle.net/10754/666955
    
    Metadata
    Show full item record
    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.
    Citation
    Array
    Publisher
    Blackwell Publishing Ltd
    Journal
    Journal of the Royal Statistical Society. Series A: Statistics in Society
    DOI
    10.1111/rssa.12646
    Additional Links
    https://onlinelibrary.wiley.com/doi/10.1111/rssa.12646
    ae974a485f413a2113503eed53cd6c53
    10.1111/rssa.12646
    Scopus Count
    Collections
    Articles

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.