Modeling travel choices to assess potential greenhouse gas emissions reductions.

Nam, Richard; Dowds, Jonathan; Lee, Brian H.Y.; Aultman-Hall, Lisa; Johnson, Alyssa · 2015 · ROSA P / University of Vermont. Transportation Research Center

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Summary

This report from the University of Vermont Transportation Research Center investigates how travel behavior modifications can reduce greenhouse gas (GHG) emissions, addressing the transportation sector’s significant contribution to U.S. emissions. While technological improvements in vehicle efficiency are promising, they require decades for market penetration. In contrast, behavioral changes—such as optimizing intra-household vehicle allocation, increasing ridesharing, and responding to built environment accessibility—offer immediate potential for reducing vehicle miles traveled (VMT) and fuel consumption. The report synthesizes findings from multiple projects utilizing data from the National Household Travel Survey (NHTS) and the Northeast Travel Choice Survey (NTCS). The first two projects analyzed intra-household vehicle allocation using 2009 NHTS data. The initial analysis assumed all household vehicles were substitutable, finding that 41% of multi-vehicle households already optimized their fleets by assigning higher VMT to more fuel-efficient vehicles. The remaining 59% could reduce fuel use by 5.2%, equating to approximately 5 billion gallons of avoided fuel nationally. A subsequent analysis restricted reallocation to vehicles of similar types (e.g., two automobiles or two SUVs) to assess feasibility. Regression models identified predictors of optimization, such as household size, income, and fuel expenditure. Results indicated that while fuel expenditure and annual VMT consistently influenced allocation decisions across vehicle types, households with trucks or those in rural areas exhibited different behavioral patterns, often driven by limited seating capacity or distinct rural travel needs. The report also details the execution of the NTCS, a survey of residents in Maine, New Hampshire, Vermont, and upstate New York, designed to capture travel behaviors in non-metropolitan areas. Data from this survey supported an analysis of rideshare potential. Discrete choice models revealed that demographic factors, employment characteristics, and built environment variables significantly influence willingness to rideshare. For instance, individuals with flexible work schedules or those living in denser tracts were more likely to consider ridesharing. Additionally, the study examined the impact of workplace and commute-corridor accessibility on annual VMT, finding that destination accessibility at home and work locations plays a crucial role in determining travel distances. The findings underscore that travel choices are a viable lever for immediate GHG reduction. Simple interventions, such as reallocating household vehicles to match fuel efficiency with usage intensity, can yield substantial fuel savings. Furthermore, the data highlights that behavioral factors are complex and influenced by socioeconomic and environmental contexts. The authors conclude that while current models explain only a portion of travel choice variability, understanding these behavioral drivers is essential for designing effective policy interventions. Continued refinement of travel behavior data collection is necessary to better target households and communities for sustainable transportation strategies.

Key finding

Reallocating household vehicles so that the most fuel-efficient vehicle is used for the highest annual mileage could save approximately 5 billion gallons of fuel nationally.

Methodology

dataset

Sample size: 15562

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• exposure measurement