Abstract:When translating travel demand model output to photochemical model input, period-based network assignment volumes must be converted to gridded-hourly vehicle emissions. A post-processor, such as the California Direct Travel Impact Model (DTIM2), is frequently used to disaggregate the period-based travel demand assignments to the fine grained spatial and temporal resolution required by the photochemical models. A recent theoretical enhancement proposed refining the temporal and spatial resolutions of travel demand model predictions using observed count data. This method provides a technique for disaggregating the period-based travel demand model assignments (e.g., AM peak, PM peak) into the hourly summaries required by most photochemical model (Lin and Niemeier, 1997). In this study we present a methodological framework for applying the new theory and discuss the results of a large-scale application empirical comparison between the standard and proposed methods for estimating regional mobile emissions in Sacramento, California. The standard method produced slightly higher estimates of daily emissions (about 1%) when compared to the emissions estimated using observed count data. However, the two approaches produced hourly emissions estimates that differed by as much as 15% in some hours.