Mining the California Truck Survey for Emissions Modeling
Research Idea Scope
This research would analyze the new California Truck Survey data to develop MOVES model inputs and compare inputs developed from this survey with other sources. It would also provide suggestions as to how a new national Vehicle Inventory and Use Survey (VIUS) (under consideration by the U.S. Department of Transportation, but not yet funded) could improve MOVES inputs with new truck activity data from other states. The California Department of Transportation (Caltrans) conducted the California Truck Survey in 2016 and 2017. Modeled after the national VIUS last conducted in 2002, this survey is providing information on vehicle and trip characteristics for heavy-duty vehicles. While a final report on the survey is being published in 2018, it will not specifically examine the data for use in developing inputs to emissions models such as MOVES or EMFAC. Telematics data, including location and engine operation, was collected for a subsample of about 650 trucks in the survey. The survey data has great potential to inform the development of MOVES inputs such as start and soak time distributions, extended idling by vehicle and use type and geography, drive cycles in specific operational contexts, and variations in activity patterns by vehicle age.
Urgency and Payoff
Heavy duty diesel vehicles account for a substantial – and increasing – fraction of emissions in state and local mobile source inventories, especially particulate matter (PM) and oxides of nitrogen (NOx). The heavy truck activity data on which the current MOVES emissions model defaults are based date from a sample of 120 trucks gathered in the late 1990s, and are both limited and potentially out of date. A recent research project, NCHRP 08-101 (Enhanced Truck Data Collection and Analysis for Emissions Modeling) explored the potential of emerging telematics data sources to update and improve the previous data and inform local MOVES model inputs. Cost, sample size, and content limitations on existing data sources, however, meant that some key questions could not be fully addressed. The project in particular flagged extended idling (truck hoteling) as a large source of emissions that also is subject to considerable uncertainty in local and even national inventories. Uncertainty also persists about emissions related to starts, and about how activity patterns vary in different geographic contexts. The California Truck Survey was noted in the NCHRP study as a potentially valuable data source, but the data were not available at the time of the study. This proposed research would take advantage of this newly available data source with the goal of improving mobile source emissions inventories and better understanding what truck-focused emission reduction strategies are needed to achieve air quality goals.
Chris Porter Cambridge Systematics, Inc. 781-539-6723