Annual Vehicle Miles Research Articles

Air pollution benefits from reduced on-road activity due to COVID-19 in the United States.

On-road transportation is one of the largest contributors to air pollution in the United States. The COVID-19 pandemic provided the unintended experiment of reduced on-road emissions' impacts on air pollution due to lockdowns across the United States. Studies have quantified on-road transportation's impact on fine particulate matter (PM2.5)-attributable and ozone (O3)-attributable adverse health outcomes in the United States, and other studies have quantified air pollution-attributable health outcome reductions due to COVID-19-related lockdowns. We aim to quantify the PM2.5-attributable, O3-attributable, and nitrogen dioxide (NO2)-attributable adverse health outcomes from traffic emissions as well as the air pollution benefits due to reduced on-road activity during the pandemic in 2020. We estimate 79,400 (95% CI 46,100-121,000) premature mortalities each year due to on-road-attributable PM2.5, O3, and NO2. We further break down the impacts by pollutant and vehicle types (passenger [PAS] vs. freight [FRT] vehicles). We estimate PAS vehicles to be responsible for 63% of total impacts and FRT vehicles 37%. Nitrogen oxide (NOX) emissions from these vehicles are responsible for 78% of total impacts as it is a precursor for PM2.5 and O3. Utilizing annual vehicle miles traveled reductions in 2020, we estimate that 9,300 (5,500-14,000) deaths from air pollution were avoided in 2020 due to the state-specific reductions in on-road activity across the continental United States. By quantifying the air pollution public health benefits from lockdown-related reductions in on-road emissions, the results from this study stress the need for continued emission mitigation policies, like the U.S. Environmental Protection Agency's (EPA) recently proposed NOX standards for heavy-duty vehicles, to mitigate on-road transportation's public health impact.

Estimating the travel demand impacts of semi automated vehicles

In this study we investigate changes in travel due to level 2 automation among owners of electric vehicles in California. Level 2 automation has the potential to reduce driver fatigue and make driving less stressful which could mean drivers choose to travel more. We use questionnaire survey data to investigate changes to long distance travel and annual vehicle miles travelled (VMT) due to automation. Results show those who report doing more long-distance travel because of automation are younger; have a lower household income; live in urban areas; own a longer-range electric vehicle; use automation in a variety of conditions; and have pro-technology attitudes and prefer outdoor lifestyles. We use propensity score matching to investigate whether automation leads to an increase in annual VMT. The results of this show 4059–4971 more miles per year among users of level 2 automation compared to owners of similar vehicles without automation.

Microsimulation Scenarios of Autonomous Vehicle Adoption and Impacts on Annual Vehicle Miles Travelled (Vmt) and Greenhouse Gas Emissions (Ghgs) in California

Microsimulation Scenarios of Autonomous Vehicle Adoption and Impacts on Annual Vehicle Miles Travelled (Vmt) and Greenhouse Gas Emissions (Ghgs) in California

Does ridesourcing impact driving decisions: A survey weighted regression analysis

Abstract The initial public offerings (IPOs) of Uber and Lyft in 2019 marked a milestone for the decade-old ridesourcing. As we start to embrace ridesourcing in our daily life, we also rearrange our daily travel amongst different modes of transportation. As the fundamental decisions in travel behavior, car ownership and car travel should be re-examined in the advent of shared mobility. In this paper, we applied a vehicle choice model that factors in ridesourcing frequency to understand the decisions about (1) how many cars an individual would declare as the primary driver of, and (2) the annual vehicle miles traveled (VMT) for all cars he or she drive. We used a subsample of the latest 2017 National Household Travel Survey (NHTS) data that focus on the Capital region (Washington, D.C. – Maryland – Virginia) as our study area. We applied a weighted regression analysis following the NHTS survey design and derived population-representative results on both decisions. In addition, we calculated the driving cost for each household vehicle based on the latest fuel economy data and incorporated driving cost into the car travel model. The results suggest that ridesourcing is associated with a smaller chance of an individual being the primary driver of a car. However, the elasticity indicates that ridesourcing usage has a small impact on the number of primarily driven cars. Furthermore, ridesourcing has no significant impact on the annual VMT, either. Driving cost, on the other hand, still plays the key role in determining driving distances.

Characterizing non-box trailer activity and aerodynamic devices for greenhouse gas emissions reductions

Add-on aerodynamic devices (cab-roof fairings, side extenders, side skirts, boat-tails and others) for heavy-duty trailers and tractors provide the potential of fuel savings, cost savings, and the reduction of greenhouse gas (GHG) emissions. The goal of this work was to evaluate potential for aerodynamic devices to provide GHG emission reductions for four trailer types: flatbed, tanker, curtainside, and container chassis. This work included an analysis of a United States (U.S.) national trailer registration database with 253,191 total trailers and a trailer activity survey of 51 fleets. Flatbed, container chassis, and tanker trailers were relatively extensively used, with 48% and 64% of fleets indicating annual vehicle miles traveled (VMT) per trailer of greater than 50,000 and 25,000 miles, respectively. Annual savings based on a 50% and 100% market penetration for the registration database in the U.S. were estimated to be approximately 37.0 to 74.1 million gallons diesel, and $92.6 to $185.1 million in fuel (~U.S.$731 per tractor-trailer), and ~0.38 to 0.76 million metric tons (MMT) CO2eq.

Evaluating emissions and sensitivity of economic gains for series plug-in hybrid electric vehicle powertrains for transit bus applications

From the design space explored for series architecture plug-in hybrid electric vehicle transit buses by the authors, one powertrain and control design is selected to provide maximum benefit to investment ratio. Sensitivity analysis is performed for this powertrain configuration. Vehicle parameters (including vehicle mass, coefficient of drag, coefficient of rolling resistance), usage parameters (drivecycle, annual vehicle miles traveled, number of recharges in a day, recharge current, and battery temperature), and economic parameters (fuel price, motor price, and battery price) are varied to understand their effect on the number of required battery replacements, net present value, payback period, and fuel consumption reduction. It is shown that battery temperature has the most significant impact, particularly on the number of battery replacements and net present value and, as such, must be well controlled in practice. It is shown that to maintain the battery at 20°C, for ambient temperatures between −5°C and 45°C, 0.8–1.8% excess fuel is required across all drivecycles for the considered plug-in hybrid electric vehicle transit bus powertrain configuration. In addition, the well-to-wheel emissions of criteria pollutants resulting from the usage of this plug-in hybrid electric vehicle transit bus in Indiana and California are calculated and compared with the conventional transit bus, using the GREET (Greenhouse Gases, Regulated Emissions and Energy Use in Transportation) Model. With a single over night charge, the plug-in hybrid electric vehicle transit bus operating in either Indiana or California produces 50% less CO2 and other greenhouse gases as compared to a conventional transit bus.

Determinants of Transport Mode Choice for Non-Commuting Trips: The Roles of Transport, Land Use and Socio-Demographic Characteristics

Despite rapid changes in vehicle technology and the expansion of IT-based mobility solutions, travel habits must be changed to address the environmental and health implications of increasing car dependency. A significant amount of research focuses on commuting, which comprises the largest share of annual vehicle miles travelled. However, non-work trips are also significant, especially when considering trip frequency. Using empirical data (N = 1298) from an urban-rural region and bivariate statistical analysis, the relationship between the land use–transport configuration (6 types) and travel behaviour patterns is examined for 14 non-work destinations. The land use characterisation used in this research includes an updated means of representing a land use mix. By defining the typologies of land use and transport for use in the analysis, the findings can be directed towards contrasting area types in the region. A strong statistically significant association between the land use–transport configuration and mode-share for 14 non-work journey purposes is found. Using regression modelling, income and car ownership are identified as key influences on travel behaviour patterns. The results of both analyses show that, for non-work trips, the transport–land use relationship is as important as key socio-demographic indicators. However, the results for reductions in car travel are relatively small for the area typologies outside the inner-city core. This indicates that efforts to provide alternatives to car travel in order to mitigate car dependency should be prioritised in these outer urban areas. Appropriate management of spatial structure for non-work activity types such that active mode use is possible is essential. This will resolve some of the important environmental and health impacts of car dependency.

Optimization of Component Sizing for a Fuel Cell-Powered Truck to Minimize Ownership Cost

In this study, we consider fuel cell-powered electric trucks (FCETs) as an alternative to conventional medium- and heavy-duty vehicles. FCETs use a battery combined with onboard hydrogen storage for energy storage. The additional battery provides regenerative braking and better fuel economy, but it will also increase the initial cost of the vehicle. Heavier reliance on stored hydrogen might be cheaper initially, but operational costs will be higher because hydrogen is more expensive than electricity. Achieving the right tradeoff between these power and energy choices is necessary to reduce the ownership cost of the vehicle. This paper develops an optimum component sizing algorithm for FCETs. The truck vehicle model was developed in Autonomie, a platform for modelling vehicle energy consumption and performance. The algorithm optimizes component sizes to minimize overall ownership cost, while ensuring that the FCET matches or exceeds the performance and cargo capacity of a conventional vehicle. Class 4 delivery truck and class 8 linehaul trucks are shown as examples. We estimate the ownership cost for various hydrogen costs, powertrain components, ownership periods, and annual vehicle miles travelled.

A comparison of the personal and neighborhood characteristics associated with ridesourcing, transit use, and driving with NHTS data

The opportunity to conduct an overarching national study to examine the characteristics of ridesourcing users and their neighborhoods was absent until the 2017 National Household Travel Survey (NHTS) dataset was released in 2018. As the 2017 NHTS combines ridesourcing with taxi and limo in the trip file, ridesourcing trip characteristics cannot be separately analyzed. Thus, the paper examines the characteristics of users and neighborhoods associated with ridesourcing trip frequency from the person file, defined as the number of rides taken in 30 days. Because the public-use NHTS dataset includes only limited information about the neighborhoods where the respondents live, additional data from the NHTS DOT files were analyzed so that the characteristics of the neighborhoods could be fully comprehended. In an effort to examine the proximity of those neighborhoods to public transit stations and stops, GIS data on transit stations and stops were analyzed from the Bureau of Transportation Statistics and the US Department of Homeland Security. A zero-inflated negative binomial (ZINB) model was used to identify the variables associated with ridesourcing trip frequency. Two similar models were used to identify the variables associated with public transit trip frequency and annual vehicle miles driven. The analysis showed that the direction of the effects of the personal and neighborhood characteristics on ridesourcing and transit trip frequency is mostly similar, but often dissimilar to their effects on miles driven. A significant finding of this research is that people living near transit stations/stops use ridesourcing more frequently.

Factors associated with round-trip carsharing frequency and driving-mileage impacts in London

This study draws on respondents from Greater London within the 2016/17 wave (n = 2640) of Britain’s Annual Survey of carsharing users, which we enrich with external data from the 2011 England and Wales Census and small-area income estimates. Focusing on round-trip carsharing users, we present multivariate analyses of frequency-of-usage of carsharing vehicles and impacts on annual vehicle miles traveled (VMT). Published attribute effects from other geographic and contextual circumstances are compiled and compared (where direct comparison is possible) with the specific attribute effects that we report in this paper. We demonstrate a statistically significant link between customer satisfaction with proximity of carsharing vehicles and VMT impacts. Car ownership (both current, and changes upon joining a carsharing service) is shown to have intuitive structural impacts. We find that frequent usage is associated ceteris paribus with increasing VMT after joining a carsharing service, and that subscribing to multiple types of carsharing is associated with frequent carsharing usage and a reduction in VMT. Interestingly, we did not find any significant effect of household income on either frequency-of-usage or VMT impacts.

Formative Experiences and the Price of Gasoline

An individual’s initial experiences with a common good, such as gasoline, can shape their behavior for decades. We first show that the 1979 oil crisis had a persistent negative effect on the likelihood that individuals that came of driving age during this time drove to work in the year 2000 (i.e., in their mid 30s). The effect is stronger for those with lower incomes and those in cities. Combining data on many cohorts, we then show that large increases in gasoline prices between the ages of 15 and 18 significantly reduce both (i) the likelihood of driving a private automobile to work and (ii) total annual vehicle miles traveled later in life, while also increasing public transit use. Differences in driver license age requirements generate additional variation in the formative window. These effects cannot be explained by contemporaneous income and do not appear to be only due to increased costs from delayed driving skill acquisition. Instead, they seem to reflect the formation of preferences for driving or persistent changes in the perceived costs of driving.

Quantifying the Sustainability, Livability, and Equity Performance of Urban and Suburban Places in California

For years, researchers and practitioners have worked toward measuring urban form, but a gap still remains in the research to quantify how urban and suburban place-types affect economic, social, and environmental outcomes at small geographic scales. To provide such analysis, this paper describes the development of a place typology and sustainability performance measurement framework for all census tracts in California. This study found there were clear trade-offs between urban and suburban living. Compared with suburbs, the households in urban places benefited from a 57.9% reduction in annual vehicle miles traveled, 37.2% lower transport-related greenhouse gas emissions per capita, and saved more than US$2,675 in annual transportation costs, while consuming less electricity (39.9%) and water per capita (63.8%). However, the cost of urban homeownership was 40% higher, despite rents being 18.5% cheaper. And although obesity and cardiovascular disease rates were 10.3% and 8.9% lower in urban places, asthma rates were 7.5% higher. From 1970 to 2015, urban housing decreased from 34% to 21%, whereas statewide it dropped 7.5%. Despite ambitious climate action and smart growth goals, the majority of growth in California continues to be in low-density suburban/rural areas, responsible for 80% of the state’s total household carbon emissions. This analysis and place typology could prove useful in identifying areas with the highest potential for lowering vehicle miles traveled and other sustainability, livability, and equity goals. This is made even more significant given California’s recent move to abolish level of service analysis for traffic impact studies.

Associations between Urban Sprawl and Life Expectancy in the United States.

In recent years, the United States has had a relatively poor performance with respect to life expectancy compared to the other developed nations. Urban sprawl is one of the potential causes of the high rate of mortality in the United States. This study investigated cross-sectional associations between sprawl and life expectancy for metropolitan counties in the United States in 2010. In this study, the measure of life expectancy in 2010 came from a recently released dataset of life expectancies by county. This study modeled average life expectancy with a structural equation model that included five mediators: annual vehicle miles traveled (VMT) per household, average body mass index, crime rate, and air quality index as mediators of sprawl, as well as percentage of smokers as a mediator of socioeconomic status. After controlling for sociodemographic characteristics, this study found that life expectancy was significantly higher in compact counties than in sprawling counties. Compactness affects mortality directly, but the causal mechanism is unclear. For example, it may be that sprawling areas have higher traffic speeds and longer emergency response times, lower quality and less accessible health care facilities, or less availability of healthy foods. Compactness affects mortality indirectly through vehicle miles traveled, which is a contributor to traffic fatalities, and through body mass index, which is a contributor to many chronic diseases. This study identified significant direct and indirect associations between urban sprawl and life expectancy. These findings support further research and practice aimed at identifying and implementing changes to urban planning designed to support health and healthy behaviors.

Adverse weather conditions and fatal motor vehicle crashes in the United States, 1994-2012.

BackgroundMotor vehicle crashes are a leading cause of injury mortality. Adverse weather and road conditions have the potential to affect the likelihood of motor vehicle fatalities through several pathways. However, there remains a dearth of assessments associating adverse weather conditions to fatal crashes in the United States. We assessed trends in motor vehicle fatalities associated with adverse weather and present spatial variation in fatality rates by state.MethodsWe analyzed the Fatality Analysis Reporting System (FARS) datasets from 1994 to 2012 produced by the National Highway Traffic Safety Administration (NHTSA) that contains reported weather information for each fatal crash. For each year, we estimated the fatal crashes that were associated with adverse weather conditions. We stratified these fatalities by months to examine seasonal patterns. We calculated state-specific rates using annual vehicle miles traveled data for all fatalities and for those related to adverse weather to examine spatial variations in fatality rates. To investigate the role of adverse weather as an independent risk factor for fatal crashes, we calculated odds ratios for known risk factors (e.g., alcohol and drug use, no restraint use, poor driving records, poor light conditions, highway driving) to be reported along with adverse weather.ResultsTotal and adverse weather-related fatalities decreased over 1994–2012. Adverse weather-related fatalities constituted about 16 % of total fatalities on average over the study period. On average, 65 % of adverse weather-related fatalities happened between November and April, with rain/wet conditions more frequently reported than snow/icy conditions. The spatial distribution of fatalities associated with adverse weather by state was different than the distribution of total fatalities. Involvement of alcohol or drugs, no restraint use, and speeding were less likely to co-occur with fatalities during adverse weather conditions.ConclusionsWhile adverse weather is reported for a large number of motor vehicle fatalities for the US, the type of adverse weather and the rate of associated fatality vary geographically. These fatalities may be addressed and potentially prevented by modifying speed limits during inclement weather, improving road surfacing, ice and snow removal, and providing transit alternatives, but the impact of potential interventions requires further research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-016-0189-x) contains supplementary material, which is available to authorized users.

Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions

Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.

Self-Driving Vehicles: Determinants of Adoption and Conditions of Usage

This study gathered empirical evidence on adoption patterns of self-driving vehicles, their likely use, and how that use might influence the amount of travel, mode choice, auto ownership, and other travel behavior decisions. Because self-driving vehicles were not yet on the market, a car technology acceptance model was applied to understand adoption and use. Researchers implemented a two-stage data collection effort. An online survey was conducted with 556 residents of metropolitan Austin, Texas, to determine intent to use. Four intent-to-use categories were determined: extremely unlikely, 18%; somewhat unlikely, 32%; somewhat likely, 36%; and extremely likely, 14%. Of those who indicated intent to use, qualitative interviews were conducted to ascertain the impact on their travel behavior. Most respondents would rather own a self-driving vehicle than use one such as Car2Go or Uber taxi. In addition, respondents reported that using a self-driving vehicle would make no change in where people would choose to live in Austin (80%), no change to their annual vehicle miles of travel (66%), and no change to the number of vehicles owned (55%).

How does fuel economy of vehicles affect urban motor vehicle travel in the USA?

This paper applies the ordinary quantile regression approach to examine the impact of fuel economy of vehicles and gasoline prices on motor vehicle travel. The dataset used includes observations with a survey date before September 2008 from the 2009 National Household Travel Survey to avoid potential problems from the wild volatility of gasoline prices in late 2008 and early 2009. The regression results indicate that for every 10 % increase in fuel economy of vehicles, annual vehicle miles traveled increase by 0.9 to 1.7 % along its distribution. For every 10 % increase in average gasoline prices, annual vehicle miles decline by 0.86 to 2.65 % along the vehicle miles traveled (VMT) distribution.

The Effect of Gasoline Taxes and Public Transit Investments on Driving Patterns

This paper analyzes how driving patterns are affected by gasoline taxes and the availability of a substitute for driving—public transportation. We develop a measure of transportation substitutability based on the difference between individuals’ predicted commute times by private and public transit, conditional upon their demographic characteristics and geographic location. Improved substitutability decreases annual vehicle miles traveled (VMT) by inducing modal shifts to public transit, though gasoline taxes are found to have a much larger impact on VMT. Our results imply that a policy that raises gasoline taxes and recycles the revenues into public transit improvements can have even larger impacts on driving patterns than either policy alone.

Does the Substitutability of Public Transit Affect Commuters’ Response to Gasoline Price Changes?

This paper analyzes how driving patterns are affected by gasoline taxes and the availability of a good substitute for driving - public transportation. We develop a measure of transportation accessibility based on the difference between individuals’ predicted commute times by private and public transit, conditional upon their demographic characteristics and geographic location. Our accessibility measure is found to decrease annual vehicle miles traveled (VMT) by inducing modal shifts to public transit, though gasoline taxes are found to have a much larger impact on VMT, both indirectly through mode shifts and directly through decreasing driving patterns. Jointly, gasoline taxes and their revenues recycled into public transit improvements can have large impacts on driving patterns.

Does the Substitutability of Public Transit Affect Commuters’ Response to Gasoline Price Changes?

This paper determines the extent to which gasoline price elasticity is affected by the availability of a substitute for driving — public transportation. Measuring the substitutability of public transportation presents an important practical difficulty. To address this, we predict individuals’ commute times by private and public transit conditional upon their observable characteristics and create a measure of substitutability between the two modes based on transit times. This allows us to measure the effect of public transportation on commuters’ sensitivity to gasoline prices. The interaction of gasoline price with our constructed substitutability measure is found to have a significant effect on annual vehicle miles traveled (VMT), indicating that investments in public transit could play an important role in altering motorists’ sensitivity to gasoline prices and increasing the effectiveness of a gasoline tax. However, we find evidence to support a policy of increasing public transit accessibility only in the presence of increased gasoline taxes.