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Predicting road system speeds using spatial structure variables and network characteristics

Hackney, Jeremy ; Bernard, Michael ; Bindra, Sumit ; Axhausen, Kay

In: Journal of Geographical Systems, 2007, vol. 9, no. 4, p. 397-417

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    Titre
    • Predicting road system speeds using spatial structure variables and network characteristics
    Auteur
    • Hackney, Jeremy. Institute for Transport Planning and Systems, ETH Hönggerberg, HIL F51.3, 8093, Zurich, Switzerland
    • Bernard, Michael. Institute for Transport Planning and Systems, ETH Hönggerberg, HIL F51.3, 8093, Zurich, Switzerland
    • Bindra, Sumit. Connecticut Transportation Institute, University of Connecticut, Castleman Building, Room 205, 261 Glenbrook Road, Unit 2037, 06269-2037, Storrs, CT, USA
    • Axhausen, Kay. Institute for Transport Planning and Systems, ETH Hönggerberg, HIL F51.3, 8093, Zurich, Switzerland
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Journal of Geographical Systems, 2007, vol. 9, no. 4, p. 397-417. Springer-Verlag
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:321299
    Summary
    Spatial regression is applied to GPS floating car measurements to build a predictive model of road system speed as a function of link type, time period, and spatial structure. The models correct for correlated spatial errors and autocorrelation of speeds. Correlation neighborhoods are based on either Euclidean or network distance. Econometric and statistical methods are used to choose the best model form and statistical neighborhood. Models of different types have different coefficient estimates and fit quality, which might affect inferences. Speed predictions are validated against a holdout sample to illustrate the usefulness of spatial regression in road system speed monitoring