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Spatio-temporal autocorrelation of road network data

Author

Listed:
  • Tao Cheng

    ()

  • James Haworth

    ()

  • Jiaqiu Wang

    ()

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

Suggested Citation

  • Tao Cheng & James Haworth & Jiaqiu Wang, 2012. "Spatio-temporal autocorrelation of road network data," Journal of Geographical Systems, Springer, vol. 14(4), pages 389-413, October.
  • Handle: RePEc:kap:jgeosy:v:14:y:2012:i:4:p:389-413
    DOI: 10.1007/s10109-011-0149-5
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    File URL: http://hdl.handle.net/10.1007/s10109-011-0149-5
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    Citations

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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 1-21, October.
    3. repec:eee:phsmap:v:501:y:2018:i:c:p:227-237 is not listed on IDEAS
    4. Ma, Tao & Zhou, Zhou & Abdulhai, Baher, 2015. "Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 27-47.

    More about this item

    Keywords

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

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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