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Temporal aggregation and spatio-temporal traffic modeling

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  • Percoco, Marco

Abstract

Traffic forecasting is crucial for policy making in the transport sector. Recently, Selby and Kockelman (2013) have proposed spatial interpolation techniques as suitable tools to forecast traffic at different locations. In this paper, we argue that an eventual source of uncertainty over those forecasts derives from temporal aggregation. However, we prove that the spatio-temporal correlation function is robust to temporal aggregations schemes when the covariance of traffic in different locations is separable in space and time. We prove empirically this result by conducting an extensive simulation study on the spatial structure of the Milan road network.

Suggested Citation

  • Percoco, Marco, 2015. "Temporal aggregation and spatio-temporal traffic modeling," Journal of Transport Geography, Elsevier, vol. 46(C), pages 244-247.
    • Handle: RePEc:eee:jotrge:v:46:y:2015:i:c:p:244-247
    DOI: 10.1016/j.jtrangeo.2015.07.001
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    References listed on IDEAS

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    1. Granger, C. W. J., 1993. "Implications of seeing economic variables through an aggregation window," Ricerche Economiche, Elsevier, vol. 47(3), pages 269-279, September.
    2. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
    3. Valter Giacinto, 2010. "On vector autoregressive modeling in space and time," Journal of Geographical Systems, Springer, vol. 12(2), pages 125-154, June.
    4. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
    5. Marco Percoco, 2007. "Evaluating forecasting accuracy of the temporally aggregated space-time autoregressive model," Applied Economics Letters, Taylor & Francis Journals, vol. 14(9), pages 637-641.
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