Spatio-temporal autocorrelation of road network data
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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- Dougherty, Mark S. & Cobbett, Mark R., 1997. "Short-term inter-urban traffic forecasts using neural networks," International Journal of Forecasting, Elsevier, vol. 13(1), pages 21-31, March.
- Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
- Jiang, Bin, 2007. "A topological pattern of urban street networks: Universality and peculiarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 647-655.
- Manuel Castells, 2010. "Globalisation, Networking, Urbanisation: Reflections on the Spatial Dynamics of the Information Age," Urban Studies, Urban Studies Journal Limited, vol. 47(13), pages 2737-2745, November.
- Zengwang Xu & Daniel Sui, 2007. "Small-world characteristics on transportation networks: a perspective from network autocorrelation," Journal of Geographical Systems, Springer, vol. 9(2), pages 189-205, June.
- Daniel Griffith, 2010. "Modeling spatio-temporal relationships: retrospect and prospect," Journal of Geographical Systems, Springer, vol. 12(2), pages 111-123, June.
- Geraldine Pflieger & Celine Rozenblat, 2010. "Introduction. Urban Networks and Network Theory: The City as the Connector of Multiple Networks," Urban Studies, Urban Studies Journal Limited, vol. 47(13), pages 2723-2735, November.
- Yang Yue & Anthony Gar-On Yeh, 2008. "Spatiotemporal traffic-flow dependency and short-term traffic forecasting," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 35(5), pages 762-771, September.
- Kamarianakis, Yiannis & Prastacos, Poulicos, 2002. "Space-time modeling of traffic flow," ERSA conference papers ersa02p141, European Regional Science Association.
- Jeremy Hackney & Michael Bernard & Sumit Bindra & Kay Axhausen, 2007. "Predicting road system speeds using spatial structure variables and network characteristics," Journal of Geographical Systems, Springer, vol. 9(4), pages 397-417, December.
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