Synthetic Fleet Generation and Vehicle Assignment to Synthetic Households for Regional and Sub-Regional Sustainability Analysis [supporting dataset]
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2024-09-30
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Abstract: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.
The total size of the zip file is 1.5 GB. The .csv, Comma Separated Value, file is a simple format that is designed for a database table and supported by many applications. The .csv file is often used for moving tabular data between two different computer programs, due to its open format. The most common software used to open .csv files are Microsoft Excel and RecordEditor, (for more information on .csv files and software, please visit https://www.file-extensions.org/csv-file-extension). The .xlsx and .xls file types are Microsoft Excel files, which can be opened with Excel, and other free available spreadsheet software, such as OpenRefine.
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. This dataset has been curated to CoreTrustSeal's curation level "C. Initial Curation." To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.11476980). NTL staff last accessed this dataset at its repository URL on 2024-12-09. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
Public Access Note: This item is made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Use the following citation:
Lu, H. (2024). Synthetic Fleet Generation and Vehicle Assignment to Synthetic Households for Regional and Sub-regional Sustainability Analysis (Version 093024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13864619
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