Improving the Resolution of Gridded-Hourly Mobile Emissions: Incorporating Spatial Variability and Hangling Missing Data

To more accurately predict hourly running stabilized link volumes for emissions modeling, a new method was recently developed that disaggregates the period-based model link volumes into hourly volumes using observed traffic count data and multivariate multiple regression (MMR). This paper examines the MMR methodology with clustering and classification analyses to account for spatial variability and to accommodate model links that do not have matching observed traffic count data. The methodology was applied to data collected in the South Air Basin. The spatial analysis resulted in identifying five clusters (or 24-h profiles) for San Diego and two clusters for Los Angeles. The MMR models were then estimated with and without clustering. For San Diego, the disaggregated model volumes with clustering were much closer to the observed volumes than those without clustering, with the exception of the a.m. period. For most hours in Los Angeles, the predicted volumes with clustering were only slightly closer to the observed volumes than those predicted without clustering, suggesting that spatial effects are minimal in Los Angeles (i.e., that 24-h volume profiles are fairly similar throughout the region) and clustering is not necessary. Finally, two classification models, one for San Diego and one for Los Angeles were developed and tested for network link data that does not have matching observed count data. The results indicate the procedure is relatively good at predicting cluster assignment for the unmatched location for Los Angeles but less accurate for San Diego.