A Weighted Autoregressive Model to Improve Mobile Emissions Estimates for Locations with Spatial Dependence

For air quality analysis, travel demand model output must be converted to the fine-grained spatial and temporal resolution required by the photochemical models. In particular, network link volume estimates by modeling period (i.e., AM, PM, off-peak) must be converted to hourly profiles. New methods for accomplishing this have not taken spatial autocorrelation into account. This research proposes a weighted autoregressive model for estimating hourly profiles when geographic ordering (i.e., links on a network) is present. An iterative maximum likelihood estimation procedure is developed and used to estimate allocation factors, which disaggregate period-based travel demand model assignments into hourly profiles, for San Diego. Three different forms of the weight matrix, W 1 (binary), W 2 (reciprocal distance), and W 3 (combination of W 1 and W 2), are tested in the analysis. The results indicate that autocorrelation from geographic ordering can be eliminated with the proposed technique.