Generating Heavy-Duty Truck Activity Data Inputs for MOVES Based on Large-Scale Truck Telematics Data
An accurate characterization of vehicle activity is crucial to the construction of a regional emissions inventory of on-road mobile sources for state implementation plans and transportation conformity analyses. However, it is a challenging task given the limited availability of vehicle activity data...
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Veröffentlicht in: | Transportation research record 2012-01, Vol.2270 (1), p.49-58 |
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Sprache: | eng |
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Zusammenfassung: | An accurate characterization of vehicle activity is crucial to the construction of a regional emissions inventory of on-road mobile sources for state implementation plans and transportation conformity analyses. However, it is a challenging task given the limited availability of vehicle activity data on a large, regional scale. Compared with data on light-duty vehicles, data on the availability of heavy-duty trucks (HDTs) are even more limited. To address this issue, this study examines truck telematics data from vehicle-tracking and -monitoring systems that have increasingly been used by commercial HDT fleets in recent years as a potential source for HDT activity data. A large-scale truck telematics data set from a collective fleet of more than 2,000 U.S. HDTs was obtained and used to develop several of the HDT activity data inputs required by the U.S. Environmental Protection Agency's motor vehicle emission simulator (MOVES) model. The developed HDT activity data inputs include vehicle miles traveled by road type, by weekday and weekend, and by hour. The data inputs also include average speed distribution as well as trip start locations and distributions. Experience working with the data set shows that the truck telematics data have both advantages and limitations. Depending on the availability and quality of the existing data sources, the truck telematics data can be used to provide, supplement, or replace some of the HDT activity data inputs required by MOVES that are developed from those existing data sources. |
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ISSN: | 0361-1981 2169-4052 |
DOI: | 10.3141/2270-07 |