Temporal aggregation and spatio-temporal traffic modeling

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, the authors argue that an eventual source of uncertainty over those forecasts derives from temporal aggregation. However, they 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. The authors prove empirically this result by conducting an extensive simulation study on the spatial structure of the Milan road network.

• Record URL:
  https://doi.org/10.1016/j.jtrangeo.2015.07.001
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0966692315001167
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09666923
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Percoco, Marco
• Publication Date: 2015-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 244-247
• Serial:
  • Journal of Transport Geography
  • Volume: 46
  • Publisher: Elsevier
  • ISSN: 0966-6923
  • Serial URL: http://www.elsevier.com/locate/jtrangeo

Subject/Index Terms

• TRT Terms: Traffic forecasting; Traffic models; Uncertainty
• Uncontrolled Terms: Spatiotemporal models
• Geographic Terms: Milan (Italy)
• Subject Areas: Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01572057
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 22 2015 9:12AM