Collecting Driving Data to Support Mobile Source Emissions Estimation
Publication: Journal of Transportation Engineering
Volume 132, Issue 11
Abstract
This paper describes a new sampling approach to collect driving data. Traditional data collection assesses the percentage of vehicle miles traveled by road facility and time of day, and collects data in proportion to how real-world driving occurs. While the traditional approach is reasonable, it would be better to establish statistical targets. Statistical targets minimize uncertainties about how well data represent real-world driving and allow confidence levels to be computed. We illustrate statistical sampling with a California study that collected over of driving data. Road load power (RLP) was estimated based on speeds and accelerations and used as a surrogate for emissions-producing behavior. The variance of RLP (RPV) was used to establish statistical targets. The new method facilitated fine-tuned data collection by facility and time of day. The research team estimated, at a 90% confidence interval, that mean RVP was within to of the true mean among facilities studied. The method ensured that key facilities were adequately represented, thus providing a data resource to build facility-specific emissions tools.
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Acknowledgments
Preparation of this paper was partially sponsored by the California Department of Transportation as part of the Caltrans/ARB Modeling Program (CAMP) under the auspices of the UC Davis–Caltrans Air Quality Project. Tom Carlson, Frank DiGenova, and Robert Dulla from Sierra Research contributed to the overall work effort; Sierra Research implemented the field data collection effort and contributed figures in this paper. D.S.E. is also with Sonoma Technology, Inc. (STI); the writers appreciate assistance provided by STI support staff. The views are those of the writers alone.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 132 • Issue 11 • November 2006
Pages: 845 - 854
Copyright
© 2006 ASCE.
History
Received: May 21, 2004
Accepted: Mar 10, 2006
Published online: Nov 1, 2006
Published in print: Nov 2006
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