The motor vehicle emission simulator (MOVES) model is the primary regulatory model for estimating automobile emissions in the United States. The model requires refined input data; otherwise, internal model assumptions that are not necessarily representative of the project being modeled can dominate the outputs. For example, project-level on-road fleet composition is highly dependent on local vehicle use; hence, MOVES default inputs and regional distributions are not likely to apply (and MOVES estimates for project-level analyses are especially sensitive to vehicle source type distribution). Unfortunately, developing project-level source type distributions can be challenging for model users. This research proposes a procedure for developing MOVES vehicle source type distribution inputs that uses the FHWA vehicle classification scheme, Environmental Protection Agency certification data, state registration data, along with on-road license plate and video data. A case study of I-85 near Atlanta, Georgia, is presented to illustrate the importance of distinguishing within light-duty vehicle classes for hydrocarbon and carbon monoxide estimations, and between the single-unit heavy-duty truck (HDT) and combination HDT classes for nitrogen oxide and particulate matter estimation. The analysis suggests that the most important work is to generate on-road distributions of HDTs with respect to single-unit and combination trucks rather than to use regional defaults. The case study results show the need for locally derived vehicle class inputs for MOVES for project-level analysis and calls for an alternative MOVES vehicle class input option that uses regulatory class distributions because the default vehicle class distribution embedded in MOVES may sometimes be unrealistic.
Developing Vehicle Classification Inputs for Project-Level MOVES Analysis
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2503 , 1 ; 81-90
2015-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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