National Household Transportation Survey Research Articles

Understanding the choice for sustainable modes of transport in commuting trips with a comparative case study

This study aims to understand the reasons behind people's modal preferences in commuting trips, which contribute to transportation-related problems and to develop and prioritize policies that increase the use of sustainable modes. For this reason, in this study, a comparative case study was conducted to evaluate the factors that lead to choosing or not choosing sustainable modes. Commuting trips in San Francisco, San Diego, Los Angeles, San Jose and Sacramento, which are large cities with high automobile dependency and many potential alternatives to driving, were examined using the National Household Transportation Survey (NHTS) and spatial data. The factor classifications suggested in the literature were shaped according to the scope of the study and examined on four main headings. Proximity analyses were completed in the GIS environment to evaluate the environmental effects. The effects of all factor parameters on modal preferences were calculated for each mode of transport in the study with Binary Logistic Regression analyses. As a result of the comparative analysis, the barriers to using sustainable transportation modes in cities and the reasons for driving are determined. As a result, long-term policies were determined and prioritized with literature to manage these causes and barriers. The results show that modal choices are affected by many different parameters, the reasons differ locationally, and accordingly, the policy measures in each region should be different.

A framework for the impact of highly automated vehicles with limited operational design domains

Abstract Highly automated vehicles (AV) are in the early stages of deployment and are likely to have significant impacts on the United States transportation system. In particular, a broad deployment of shared, on-demand AVs might significantly impact vehicle ownership and transportation energy consumption; projecting these impacts is essential for climate, infrastructure, and policy planning. However, it seems increasingly likely that AVs will be deployed gradually over a period of decades, in which case there may be geographic or functional variation in their availability. This might occur for a combination of technological, policy, and economic reasons. This manuscript seeks to advance a new framework for projecting AV impacts, with a particular focus on energy consumption impacts. Specifically, we introduce a framework for AV impacts that allows for AVs catering to specific operating environments or ride types. As a demonstration of this framework, we use the 2009 National Household Transportation Survey (NHTS) to segment US household travel demand based on built environment and ride length. Our framework allows us to specify AV “availability” for each population segment and ride type and use that information to predict the impact of AVs. We analyze a case scenario where shared, on-demand AVs are mostly suited for short trips in highly urbanized environments. We project the impact on household relocation, private vehicle ownership, induced travel demand, and fuel consumption. Utilization of this framework would help identify policy levers for sustainable deployment of AVs.

Moving Toward Physical Activity Targets by Walking to Transit: National Household Transportation Survey, 2001–2017

Public transportation systems can help people engage in physical activity. This study assesses sociodemographic correlates and trends in the daily time spent walking to and from transit in the U.S. from 2001 to 2017. This cross-sectional study used data from the 2001, 2009, and 2017 National Household Transportation Survey. Data were analyzed in 2019 to assess the daily level of physical activity attained solely by walking to and from transit. Regression models were used to examine predictors of daily transit‒associated walking. Compared with the full National Household Transportation Survey sample, transit users who walked to and from transit tended to be younger, from households earning <$25,000 per year, in areas with rail infrastructure, and did not have a household-owned car. Transit walkers spent a median of 20 minutes per day (95% CI=18.5, 21.5) walking to and from transit in 2017, compared with a median of 19 minutes (95% CI=17.5, 20.5) in 2001. Among transit walkers, daily transit-associated physical activity was 27% higher for those residing in areas with rail infrastructure (adjusted coefficient=1.27, 95% CI=1.11, 1.46) and 34% higher for those from households earning <$25,000 per year than those earning >$99,999 per year (adjusted coefficient=1.34, 95% CI=1.15, 1.56). As documented in a growing literature, most public transit trips include at least some walking; thus, efforts to encourage transit use are favorable to public health. Continued monitoring by transportation surveys is important as new forms of mobility and changing demographics may impact future transit use and associated physical activity.

An increasing gasoline price elasticity in the United States?

Abstract Drawing on the 2009 and 2017 waves of the National Household Transportation Survey, this paper is concerned with modeling the fuel price elasticity, allowing for differential estimates in its magnitude over time and across households. We find an elasticity close to zero for the year 2009, which increases to upwards of −0.3 by the year 2017. We explore the robustness of this result to different model specifications and estimation techniques, including instrumental variable estimation to account for the possible endogeneity of fuel prices, as well as quantile regression to account for heterogeneity according to driving intensity. While a similar pattern of increasing elasticity over time emerges across all these models, the quantile model suggests an inverse relationship between the magnitude of the elasticity and miles driven in 2017. As demonstrated with a back of the envelope calculation, one implication of this pattern is a more muted effectiveness of fuel taxation than implied by the estimates of a standard mean regression.

An Increasing Gasoline Price Elasticity in the United States?

Drawing on the 2009 and 2017 waves of the National Household Transportation Survey, this paper is concerned with modeling the fuel price elasticity, allowing for differential estimates in its magnitude over time and across households. We find a small but statistically significant mean elasticity of about -0.05 for the year 2009, which increases over fivefold to -0.26 by the year 2017. We explore the robustness of this result to different model specifications and estimation techniques, including instrumental variable estimation to account for the possible endogeneity of fuel prices, as well as quantile regression to account for heterogeneity according to driving intensity. While a similar pattern of substantially increasing elasticity emerges across all these models, the quantile model suggests an inverse relationship between the magnitude of the elasticity and miles driven in 2017. As demonstrated with a back of the envelope calculation, one implication of this pattern is a more muted effectiveness of fuel taxation than implied by the estimates of a standard mean regression.

Neighborhood density and travel mode: new survey findings for high densities

ABSTRACTAt high densities, land uses get close enough to each other to support walk, bike, and transit modes above 60% of total trips. The San Francisco Bay Area census was used to define five density levels: rural, exurban, suburban, central city, and urban core. The urban core definition, over 50 persons per neighborhood acre, is much denser than in other research. The California Household Transportation Survey supplied new data on block group area, population, trip stages, trip distances, trip time, and travel mode by density. The National Household Transportation Survey supplied block group population, density, travel mode, and income data. Both sources show a strong nonlinear relationship going from rural to urban core: auto miles and trips decrease as walk and transit miles and trips increase. With density, people travel fewer miles and spend less time traveling. High-income households in dense areas travel far fewer miles than those living at higher densities. With sufficient density, complementary features play a role in furthering mode shift. For planning purposes, the need for parking greatly declines. The findings are a basis for similar research elsewhere on high densities and complementary features.

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.

Weather, Traffic Accidents, and Climate Change

We exploit random daily variation in weather to document the relationship of temperature, rainfall, and snowfall with traffic accidents and travel demand. Using information on 46.5 million accidents from the State Data System of police reported accidents for 20 states and travel demand for 207,455 households included in the National Household Transportation Survey, we find unanticipated effects of weather on accidents and their severity. Our estimates suggest that while warmer temperatures and reduced snowfall are associated with a moderate decline in non-fatal accidents, they are also associated with a significant increase in fatal accidents. This increase in fatalities is due to a robust positive relationship between fatalities and temperature. Half of the estimated effect of temperature on fatalities is due to changes in the exposure to pedestrians, bicyclists, and motorcyclists as temperatures increase. The application of these results to middle-of-the-road climate predictions suggests that weather patterns for the end of the century would lead to 603 additional fatalities per year. Between 2010-2099, the present value social cost of all types of accidents caused by climate change is $58 billion.

Estimating the Impact of Electric Vehicle Smart Charging on Distribution Transformer Aging

This paper describes a method for estimating the impact of plug-in electric vehicle (PEV) charging on overhead distribution transformers, based on detailed travel demand data and under several different schemes for mitigating overloads by shifting PEV charging times (smart charging). The paper also presents a new smart charging algorithm that manages PEV charging based on estimated transformer temperatures. We simulated the varied behavior of drivers from the 2009 National Household Transportation Survey, and transformer temperatures based an IEEE standard dynamic thermal model. Results are shown for Monte Carlo simulation of a 25 kVA overhead distribution transformer, with ambient temperature data from hot and cold climate locations, for uncontrolled and several smart-charging scenarios. These results illustrate the substantial impact of ambient temperatures on distribution transformer aging, and indicate that temperature-based smart charging can dramatically reduce both the mean and variance in transformer aging without substantially reducing the frequency with which PEVs obtain a full charge. Finally, the results indicate that simple smart charging schemes, such as delaying charging until after midnight can actually increase, rather than decrease, transformer aging.

Causality between Built Environment and Travel Behavior

The goal of this study was to explore the causality between the built environment and three transportation modes (private vehicles, mass transit, and nonmotorized modes such as bicycling and walking) while accounting for sociodemographic characteristics for an understanding of what factors may foster transit and nonmotor use. Data from the 2009 National Household Transportation Survey combined with Los Angeles County, California, land use data were used to estimate a recursive structural equations model for an examination of causality direction and for a quantification of the built environment effect and residential self-selection effect. Results suggest that urban planning strategies that promote high population and employment density, land use mix, high four-way intersection density, and road density as well as good transit service can either significantly encourage transit use, bicycling, and walking or reduce vehicle miles traveled. However, the relatively small effect of the size of the built environment on travel behavior implies that achieving the above planning goals may cause considerable residential relocation, which contributes to the major part of observed travel behavior changes.

An Analysis of Car and SUV Daytime Parking for Potential Opportunity Charging of Plug-in Electric Powertrains

Using the 2009 National Household Transportation Survey (NHTS), an analysis of opportunity charging potential during daytime, for the time interval when a car or SUV is parked for the longest duration, is presented here. We focus on charging at 3.3 kW or less, either using the charger at the dwelling unit a second time per day (a one charger solution), or using a second charge point at work. Our earlier research with the 2009 NHTS indicates that nearly 60% of vehicles within this sample were driven to work, or returned to home between 6:00 AM and 6:00 PM. In this analysis we consider the potential for daytime charging before summertime afternoon utility load peaks, anticipating that Public Utility Commissions (PUCs) supporting smart grid pricing strategies may impose much higher electricity costs at these times. We consider kW ratings of typical opportunity chargers versus overnight chargers. We consider the plug-in hybrid with 28 km of urban electric range (PHEV28), the extended range electric vehicle with 56 km of universal all-electric operation capability (EREV56) and the battery electric vehicle with 117 km of electric range (BEV117). Electricity demand and gasoline fueled miles reduction is examined for the average circumstance in two daily distance brackets (48-80 km, 80-160 km) and for two charging behaviors – (1) overnight and (2) both overnight and during the longest duration parking event of daytime hours, from 6 am to 6 pm.

Household Vehicle Bundle Choice and Gasoline Demand: A Discrete-Continuous Approach

Accurately estimating the price elasticity of demand for gasoline is fundamental in many different policy settings, in that how consumers respond to changing gas prices significantly affects the outcome of policy forecasts and debates. This paper simultaneously addresses four common modeling assumptions frequently used in gasoline demand estimation that cause the researcher to underestimate this elasticity. These are (i) ignoring the role of bundle effects in the driving decision; (ii) over-aggregation of the choice set; (iii) ignoring the inter-relatedness of the decisions about how much to drive and what vehicles to purchase; and (iv) failing to account for unobserved vehicle attributes. While methods typically found in the literature can only deal with a subset of these issues at a time, the empirical technique I employ (which is based on revealed preference inequalities) easily allows for an integrated analysis that deals with all of these concerns. Furthermore, this approach allows me to test the impact of each of these assumptions, both independently and jointly, on the elasticity estimate. By utilizing disaggregate household data from the National Household Transportation Survey in 2001 and 2009, I demonstrate that the researcher may underestimate the elasticity up to 66% if these aspects of the vehicle purchase and driving decisions are not taken into consideration.

A quantile regression analysis of the rebound effect: Evidence from the 2009 National Household Transportation Survey in the United States

This paper applies quantile regression method to measure the rebound effect and differentiate it with respect to demand for mobility using the 2009 National Household Transportation Survey (NHTS). The quantile regression results indicate that the rebound effect varies with the distribution of vehicle miles traveled (VMT), ranging between 0.11 and 0.19. Road network density and population density also play an important role in determining travel demand. Regression results indicate that travelers living in areas with higher road network density travel more miles although this positive impact consistently declines along the VMT distribution. Travelers living in areas with population density of at most 3000 persons per square miles travel more miles than those living in higher density areas. The quantile regression results also indicate that the impact of income is positive but declines consistently along the VMT distribution.

The Effect of Sprawl on Private-Vehicle Commuting Outcomes

Amongst the hypothesized beneficial impacts of sprawl on urban quality of life is a decrease in commuting times. Unfortunately, empirical evidence to substantiate the commuting time benefits of sprawl is scant. To address this void in the urban planning literature, a multilevel approach is adopted to study how sprawl affects the duration and length of private-vehicle commutes. Using microdata from the 2001 National Household Transportation Survey for individuals and microdata on various measures of sprawl for metropolitan areas, multilevel models of commuting times and distances are estimated for a subsample of private-vehicle commuters. Results somewhat support the hypothesis that sprawl decreases commuting times for private-vehicle commuters—employment decentralization is associated with shorter commuting times. However, this finding is based on the statistical significance of one of the sprawl measures; not on its practical significance in explaining commuting times which is, in fact, negligible. Coupling this with the finding that the multilevel model only explained about 5% of the variance in private-vehicle commuting times suggests that there is considerable room available for improving the model specification.

Net Air Emissions from Electric Vehicles: The Effect of Carbon Price and Charging Strategies

Plug-in hybrid electric vehicles (PHEVs) may become part of the transportation fleet on time scales of a decade or two. We calculate the electric grid load increase and emissions due to vehicle battery charging in PJM and NYISO with the current generation mix, the current mix with a $50/tonne CO(2) price, and this case but with existing coal generators retrofitted with 80% CO(2) capture. We also examine all new generation being natural gas or wind+gas. PHEV fleet percentages between 0.4 and 50% are examined. Vehicles with small (4 kWh) and large (16 kWh) batteries are modeled with driving patterns from the National Household Transportation Survey. Three charging strategies and three scenarios for future electric generation are considered. When compared to 2020 CAFE standards, net CO(2) emissions in New York are reduced by switching from gasoline to electricity; coal-heavy PJM shows somewhat smaller benefits unless coal units are fitted with CCS or replaced with lower CO(2) generation. NO(X) is reduced in both RTOs, but there is upward pressure on SO(2) emissions or allowance prices under a cap.

Small area estimates of daily person-miles of travel: 2001 National Household Transportation Survey

The National Household Transportation Survey (NHTS) was designed at the national level, and for most states it does not have a large enough sample to produce reliable estimates, especially for subdomains (e.g., age groups) within a state. Using the 2001 NHTS, we produced small area estimates (SAEs) of the percentage of persons among four age groups (17 or younger, 18–39, 40–54, and 55 or older) having high daily person-miles of travel (more than 87.5 miles a day, which is the 90th percentile for daily person-miles traveled) and associated prediction intervals for all 50 states and the District of Columbia. The survey weighted hierarchical Bayes (Folsom et al., Proc of the Sect on Surv Res Methods of the Am Stat Assoc 371–375, 1999) small area estimation (SAE) methodology was used to produce state-level SAEs. This paper describes the methodology and shows that SAE can be an effective technique for producing reliable state-level estimates from large, national surveys like the NHTS. In particular, the prediction interval relative widths for SAEs were, on average, 31–48% narrower than the corresponding design-based confidence interval widths, whereas for small states the reduction was around 47–63%.

Travel behavior of immigrants: An analysis of the 2001 National Household Transportation Survey

The purpose of this paper is to examine the relationships between travel behavior and immigrant status. The National Household Travel Survey (NHTS) allows us to explore the relationships between travel behavior and characteristics that are usually hard to discern in surveys with smaller samples. The correlation between travel behavior and immigrant characteristics such as place of birth and year of immigration in the US was tested while controlling for spatial and socio-demographic variables. The effects of place of birth and year of arriving to the US were found to be significant for some places of birth and for immigrants who entered the US in recent years. Understanding the differences in travel behavior and the possible explanations for these differences can help in modeling travel demand, finding policies best suited to meeting the travel needs of foreign-born communities, and addressing environmental justice concerns.

Rear-Seat Motor Vehicle Travel in the U.S.: Using National Data to Define a Population at Risk

Recent studies suggest that the relative protection offered by rear seating in motor vehicle crashes has decreased, potentially reflecting disproportionate advancements in front-seat safety technology. Safe adaptation of advanced front-seat restraint systems for the rear-seat environment will require exposure data that are currently unavailable. This study uses national data to quantify rear-seat occupancy patterns, restraint use, and annual travel exposure in the U.S. in order to support the development of advanced crash protection systems for rear-seat motor vehicle occupants. Data from the 2000-2006 National Automotive Sampling System Crashworthiness Data System and 2001 National Household Transportation Survey were analyzed in 2008 to quantify occupancy patterns (e.g., seat position, restraint use) and annual person-trips for rear-seat passengers in the U.S. The overall proportion of person-trips by rear-seat occupants is relatively low (12.9%); however national at-risk exposure remains significant (approximately 39 billion annual person-trips). Annual rear-seat travel exposure is similar among children < or = 12 years and adults (18.9 vs 19.1 billion person-trips) despite the fact that children are proportionally much more likely to ride in rear positions (79.3% vs 7.4%). Restraint use among adult rear-seat occupants was also much lower than among front-seat occupants (50.4% vs 82.2%). While rear-seat occupancy is relatively low compared with front-seat occupancy at-risk rear-seat travel by both child and adult passengers in the U.S. remains significant. Restraint use by rear-seat occupants is much lower than that among front-seat passengers, particularly among adults and older children, substantially increasing injury risk. Development of future crash protection systems for rear-seat passengers must account for these exposure patterns to ensure safe and effective integration into production vehicles.

True Walking Distance to Transit

People riding transit in the city of Detroit walk on average 0.8 miles (1.3 km) per round trip. The straight-line walking distance was found by buffering the bus stop locations and comparing them to the weighted US Census blocks. However, the true walking path follows the street pattern. Rather than undertaking network analysis, which would require connecting all addresses in the city with all bus stops, a Monte Carlo simulation was performed in geographic information system with random addresses. The simulation was performed over several addresses until convergence was achieved. The distances were converted to walking times and compared to the US National Household Transportation Survey.

Urban Sprawl and Miles Driven Daily by Teenagers in the United States

Urban sprawl's association with increased automobile reliance and daily mileage is well established among adults. However, sprawl's specific impact on teen driving exposure is unknown. Teen driver fatality rates per mile driven are significantly higher than adults, making the identification of environmental influences on travel behavior particularly important in this age group. Driving and demographic data for 4528 teens (weighted=10.5 million) aged 16-19 years were obtained from the 2001 National Household Transportation Survey (NHTS). County-level sprawl was measured using an index developed by Ewing et al. The association between daily miles driven by teens and sprawl, controlling for demographic characteristics, was modeled using ordinal logistic regression. The predicted probability of driving >20 miles in counties with varying degrees of sprawl also was calculated. Of the surveyed teens, 48% did not drive, 27% drove <20 miles/day, and 25% drove >20 miles/day. Of the 52% of teens who reported driving, the average distance driven was 15.6 miles/day. More-pronounced sprawl was associated with increased daily mileage (p<0.001). Overall, teens in sprawling counties were more than twice as likely to drive >20 miles/day than teens in compact counties. This trend was most prominent among the youngest drivers. For example, the predicted probability of boys aged 16-17 years driving >20 miles per day varied from 9% to 24% in compact versus sprawling counties. Sprawl is associated with increased daily mileage by teen drivers. Given the stark relationship between driving exposure and fatality risk among teens, increased efforts to understand and modify the effects of sprawl on adolescent driving behavior are necessary.