Temporal aggregation and spatio-temporal traffic modeling

• Recent literature has proposed spatial interpolation techniques traffic prediction.
• A source of uncertainty may be temporal aggregation.
• Spatio-temporal correlation function is robust to temporal aggregation if covariance is separable.
• Robustness to aggregation is proven by simulation on the spatial structure of the Milan road network.

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.