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RECORD NUMBER: 3 OF 8

OLS Field Name OLS Field Data
Main Title Integrating Travel Demand Forecasting Models with GIS to Estimate Hot Stabilized Mobile Source Emissions.
Author Bachman, W. ; Sarasua, W. ; Washington, S. ; Guensler, R. ; Hallmark, S. ;
CORP Author Georgia Inst. of Tech., Atlanta. School of Civil and Environmental Engineering.;Environmental Protection Agency, Research Triangle Park, NC. Air Pollution Prevention and Control Div.
Publisher 1997
Year Published 1997
Report Number EPA-R-817732; EPA/600/A-96/130;
Stock Number PB97-193627
Additional Subjects Travel demand ; Mobile pollutant sources ; Trip distribution models ; Vehicle air pollution ; Geographic information systems ; Trip forecasting ; Travel patterns ; Automobile exhaust ; Emission factors ; Carbon monoxide ; Spatial variations ; Temporal variations ; Estimation ; Mathematical models ;
Internet Access
Description Access URL
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100UDF0.PDF
Holdings
Library Call Number Additional Info Location Last
Modified
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Status
NTIS  PB97-193627 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 12/22/1997
Collation 15p
Abstract
The paper discusses integrating travel demand forecasting models with the Geographic Information System (GIS) to estimate hot stabilized mobile source emissions. In developing a regional mobile source emissions model using a GIS framework, the emissions model is designed to improve emission estimates by accounting for the spatial and temporal effects of a variety of vehicle activities, environmental factors, and vehicle and driver characteristics. While a description of the overall modeling approach is given, the emphasis of the paper is to describe the hot stabilized emissions estimation process and the role of travel demand forecasting models. Although travel demand forecasting models were designed for predicting future capacity requirements, they also provide useful information needed for mobile source emissions estimates. Improvements to travel demand forecasting models to more accurately predict hot stabilized emissions are also discussed.