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Spatio-temporal autocorrelation of road network data

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  • Tao Cheng

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  • James Haworth

    ()

  • Jiaqiu Wang

    ()

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    Abstract

    Modelling autocorrelation structure among space–time observations is crucial in space–time modelling and forecasting. The aim of this research is to examine the spatio-temporal autocorrelation structure of road networks in order to determine likely requirements for building a suitable space–time forecasting model. Exploratory space–time autocorrelation analysis is carried out using journey time data collected on London’s road network. Through the use of both global and local autocorrelation measures, the autocorrelation structure of the road network is found to be dynamic and heterogeneous in both space and time. It reveals that a global measure of autocorrelation is not sufficient to explain the network structure. Dynamic and local structures must be accounted for space–time modelling and forecasting. This has broad implications for space–time modelling and network complexity. Copyright Springer-Verlag 2012

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    File URL: http://hdl.handle.net/10.1007/s10109-011-0149-5
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    Bibliographic Info

    Article provided by Springer in its journal Journal of Geographical Systems.

    Volume (Year): 14 (2012)
    Issue (Month): 4 (October)
    Pages: 389-413

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    Handle: RePEc:kap:jgeosy:v:14:y:2012:i:4:p:389-413

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    Web page: http://www.springerlink.com/link.asp?id=103079

    Related research

    Keywords: Spatial autocorrelation; Network structure; Space–time autocorrelation; Space–time modelling; Travel time prediction; Network complexity; R41; C23; C52;

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    Cited by:
    1. Jenelius, Erik & Koutsopoulos, Haris N., 2013. "Travel time estimation for urban road networks using low frequency probe vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 64-81.
    2. Christopher T. Boyko & Rachel Cooper, 2013. "Density and Decision-Making: Findings from an Online Survey," Sustainability, MDPI, Open Access Journal, vol. 5(10), pages 4502-4522, October.

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