Synthetic Fleet Generation and Vehicle Assignment to Synthetic Households for Regional and Sub-regional Sustainability Analysis

In this study, a modeling framework was developed to generate high-resolution synthetic fleets, for use with synthetic household modeling in activity-based travel models, by integrating various data sources. The synthetic households were generated by pairing household locations and demographic attributes, and synthetic fleets were assigned to the households so that travel demand model outputs would have vehicles associated with each model-predicted tour for energy and emissions analysis. The CO emissions were modeled for each vehicle and each link traversed by vehicles as predicted by the travel demand model, and the results of the synthetic fleet (by employing Monte Carlo simulations and Bootstrap techniques) were compared with those from standard regional and sub-regional fleet configurations. The results demonstrated that using a traditional sub-regional fleet scenario produced 30% higher predicted emissions than when the synthetic fleet was employed with predicted vehicle trips, and that using a regional average fleet (applied throughout the region) produced emissions that were more than 50% higher than synthetic fleet emissions. Lowest household emissions were associated with low-income and non-working households, and highest emissions were associated with moderate-income households and one-person high income household groups. The results presented in the research are not necessarily conclusive, because the licensed vehicle data procured for Atlanta appear to be biased toward older vehicles. Model year penetration rates are accounted for in these analyses, but the authors believe that the variability in the registration mix for newer vehicles is likely underestimated in the data procured for these analyses. The authors conclude that access to statewide registration data will be required to remove potential biases that exist in licensed private data sets. Nevertheless, the study does demonstrate that properly pairing vehicle model years with the most active households (and their daily trips) significantly impacts energy and emissions analysis. View the NCST Project Webpage