National Household Travel Survey Research Articles

A novel maximum likelihood based probabilistic behavioral data fusion algorithm for modeling residential energy consumption.

The current research effort is focused on improving the effective use of the multiple disparate sources of data available by proposing a novel maximum likelihood based probabilistic data fusion approach for modeling residential energy consumption. To demonstrate our data fusion algorithm, we consider energy usage by fuel type variables (for electricity and natural gas) in residential dwellings as our dependent variable of interest, drawn from residential energy consumption survey (RECS) data. The national household travel survey (NHTS) dataset was considered to incorporate additional variables that are not available in the RECS data. With a focus on improving the model for the residential energy use by fuel type, our proposed research provides a probabilistic mechanism for appropriately fusing records from the NHTS data with the RECS data. Specifically, instead of strictly matching records with only common attributes, we propose a flexible differential weighting method (probabilistic) based on attribute similarity (or dissimilarity) across the common attributes for the two datasets. The fused dataset is employed to develop an updated model of residential energy use with additional independent variables contributed from the NHTS dataset. The newly estimated energy use model is compared with models estimated RECS data exclusively to see if there is any improvement offered by the newly fused variables. In our analysis, the model fit measures provide strong evidence for model improvement via fusion as well as weighted contribution estimation, thus highlighting the applicability of our proposed fusion algorithm. The analysis is further augmented through a validation exercise that provides evidence that the proposed algorithm offers enhanced explanatory power and predictive capability for the modeling energy use. Our proposed data fusion approach can be widely applied in various sectors including the use of location-based smartphone data to analyze mobility and ridehailing patterns that are likely to influence energy consumption with increasing electric vehicle (EV) adoption.

Estimating the tipping point for lithium iron phosphate batteries

Uncertainty surrounding NMC cathode chemistry prices have prompted increasing interest in less expensive alternative technologies. Chief among these is lithium iron phosphate (LFP), a chemistry that offers a cost advantage at the expense of energy density. We estimate which chemistry offers a lower cost at targeted vehicle ranges consistent with those consumers can expect from internal combustion engine vehicles. Our model – which considers tradeoffs between battery capacity and weight – enumerates a range ‘tipping point’ of 373.52 miles, beyond which NMC batteries consistently demonstrate a cost advantage over LFP batteries, despite the latter's reliance on less costly minerals. Using this tipping point as a benchmark, we leverage trip-level data from the National Household Travel Survey to explore which U.S. households may benefit from EVs equipped with LFP versus NMC batteries. Among multi-vehicle households, only 1 % of all trips taken exceed 160.53 miles, a figure analogous to our most conservative tipping point. To the extent that EVs may be utilized for relatively short commutes or as secondary or tertiary vehicles, our results suggest that LFP batteries can offer lower costs relative to NMC batteries while satisfying most households' travel demands. We subsequently discuss the policy implications of these findings.

Rural travel burdens in the United States: Unmet need and travel costs

Transportation accessibility, or the ease of reaching valued destinations, is a critical determinant of a person's ability to satisfy their essential needs. A lack of accessibility can result in travel burdens such as high transportation costs or unmet needs and adversely affect well-being. Prior research establishes the inverse relationship between travel burdens and access to transportation options such as public transit and proximity to destinations as well as a person's resources, including their income and access to a personal vehicle. Although travel behavior is understood to differ across rural versus urban contexts, little is known about the nature of travel burdens in rural communities. Using the 2017 National Household Travel Survey, this study quantifies and compares travel burdens in rural versus nonrural communities in the United States. In each context, we quantify and compare i) the magnitude of travel burdens, ii) who experiences travel burdens, and iii) the individual and environmental factors that are associated with travel burdens. We find higher rates of burdensome travel outcomes among rural residents. People who live in rural areas are more likely to report burdensome travel costs and unmet travel needs due to a lack of transportation options compared to people living in nonrural areas, and these differences are exacerbated for people without car access. Dispersed rural contexts are unique in that they exhibit a combination of higher rates of financial burden and unmet need relative to urban contexts. Within rural areas, financial burdens and unmet need are less prevalent for those who live in a small town when compared with those living in more dispersed areas, which suggests that even a small concentration of services and opportunities may facilitate greater access. Collectively, our results highlight the need for research that attends to context-specific needs and strategies to address travel burdens in rural communities.

Invisible Rides: How Car-Less Americans Access Cars

How and why do zero-car households seek car access? We used a national online survey of 830 American adults and interviews with twenty-nine low- and moderate-income travelers about their car access behaviors to answer this question. We validated our findings with the 2017 National Household Travel Survey. Respondents got rides, borrowed cars, and used ride-hail to access grocery trips, social/recreational activities, and medical care. While most interviewees intend to purchase a vehicle in the future, they also desire better transit, suggesting that households without cars do not necessarily prefer car ownership.

Household characteristics and electric vehicle adoption: changes from 2017 to 2022

ABSTRACT State and federal agencies have set several ambitious goals regarding electric vehicle sales in the United States. For example, the Biden Administration has set a target that 50% of new vehicle sales be electric by 2030. California aims to have 100% of new vehicles be zero-emission by the 2035 model year. This paper uses data from the 2017 and 2022 National Household Travel Surveys to document the extent of progress achieved in pursuit of these goals. These nationally representative surveys indicate that electric vehicle ownership has increased for both homeowners and renters, across all income groups, in single- and multi-vehicle households, and for urban and rural households. The results also highlight a few areas that vehicle manufacturers and policymakers need to address to accelerate the transition to a vehicle fleet that is less dependent on fossil fuels.

An In-Depth Look at the Trip-Deprived People of the United States

Social equity is fundamental to achieving sustainability. However, the social dimension of sustainability has received less attention than the environmental and economic dimensions. In the United States, policies mandate equitable distribution of benefits from transportation investments among all people, including the underserved populations consisting of people with disabilities, poor people, minorities, and older adults. These populations were historically considered transportation-disadvantaged because of their inability to travel like others. However, until the release of the 2022 National Household Travel Survey (NHTS) data in November 2023, there were no national data to comprehensively examine the validity of the assumptions about people’s inability to travel. By including a special-topic question on equity for the first time that enquires about people taking fewer-than-planned trips in a 30-day period for certain reasons, the 2022 NHTS makes it possible to take a deeper look at the trip-deprived Americans. This research uses logit models and confirmatory factor analysis with a national sample of more than 11,000 NHTS respondents to examine the personal, household, and geographic area characteristics of the trip deprived. The models controlled for variations in travel need. The results show that people with disabilities, unemployed people, people with low income, Black people, and people without cars are at a higher risk of being trip-deprived. Similar evidence was not found for older adults. Geographic area characteristics are not as important as the personal and household characteristics, but they also provide important insights for transportation planning purposes.

Socioeconomic variations in walking rates in the United States: Recent evidence from the 2022 National Household Travel Survey

IntroductionWe use the 2022 National Household Travel Survey for the USA—the first since the peaking of the COVID-19 pandemic—to examine variations in walking rates related to differences in socioeconomic characteristics. Our analysis provides the most recent national estimates of socioeconomic variation in walking, by far the most important form of daily physical activity in the USA. MethodsWe used graphical bivariate analysis and multiple regressions to explore the relationship between each of the socioeconomic variables and the following daily walk rates: the number of walk trips, time walked, and distance walked. We also analyzed three binary variables indicating whether a person made at least one walk trip during the travel day, achieved the equivalent of 150 min of walking per week, and walked on five or more days during the previous 30 days. ResultsHigher education levels, not owning a car, not having a driver's license, not working, working from home, and high density are strongly related to higher levels of walking. We found no significant differences or mixed results for gender, age, race, and income. Because the 2022 NHTS incorporated significant changes in methodology, sample size, survey questions, and variable definitions, its estimated trip rates cannot be compared directly to the 2017 NHTS—the last survey prior to COVID. Moreover, the 2022 NHTS greatly underestimated walk trips because—unlike previous NHTS surveys—it omitted the trip diary as well as multiple prompts to remember short trips, especially walk trips. ConclusionsThe relationships we estimated between various walk rates and the nine socioeconomic variables for 2022 were generally consistent with the results of previous studies, including those examining the 2001, 2009, and 2017 NHTS. That suggests that these relationships have remained stable over time—even if absolute rates of walking have changed, as indicated by other studies.

Unlocking Trends: Socio-Demographic Insights into Bike Sharing from the 2017 National Household Travel Survey

The implementation of bike-sharing systems represents a novel strategy aimed at enhancing urban mobility by offering shared bicycle services to improve accessibility and user convenience. Nevertheless, there has been a limited exploration into the user demographics of bike-sharing programs on a national scale. This study investigates the socio-demographic characteristics of bike-sharing system users, leveraging the 2017 National Household Travel Survey and negative binomial regression models. It finds that bike-sharing usage is more common among users than non-users, with African American and Hispanic individuals using the system more than other racial groups. Furthermore, low-income individuals (<USD 15,000) and residents of high-density areas (≥25,000 per square mile) show a higher tendency towards bike sharing. The research highlights the lack of national-level studies on bike-sharing user demographics and suggests that future policy and planning should address the needs of specific populations, aiming to enhance urban mobility and accessibility through bike-sharing services.

Predicting hurricane evacuation behavior synthesizing data from travel surveys and social media

Evacuation behavior models estimated using post-disaster surveys are not adequate to predict real-time dynamic population response as a hurricane unfolds. With the emergence of ubiquitous technology and devices in recent times, social media data with its higher spatio-temporal coverage has become a potential alternative for understanding evacuation behaviour during hurricanes. However, these data are often associated with selection bias and population representativeness issues. To that extent, the current study proposes a novel data fusion algorithm to combine heterogeneous data sources from transportation systems and social media, in a unified framework to understand and predict real-time population response during hurricanes. Specifically, Twitter data of 2300 users are collected for evacuation response during Hurricane Irma and augmented behaviourally (probabilistically) with a representative National Household Travel Survey (NHTS) data, thus creating a hybrid dataset to improve the representativeness as well as provide a rich set of explanatory variables for understanding the evacuation behavior. The fusion process is conducted using a probabilistic matching method based on a set of common attributes across NHTS and Twitter. The fused dataset is employed to estimate the evacuation model and a comparison exercise is conducted to evaluate the performance of the model via fusion. The model fitness measures clearly demonstrate the improvement in data fit for the evacuation model through the proposed fusion algorithm. Further, we conduct a prediction assessment to illustrate the applicability of the proposed fusion technique and the results clearly highlight the improvement in the evacuation prediction rate achieved through the fused models. The proposed data-driven methods will enhance our ability to predict time-dependent evacuation demand for better hurricane response operations such as targeted warning dissemination and improved evacuation traffic management, allowing emergency plans to be more adaptive.

Estimating and Implementing a Vehicle-Type Model in an Activity-Based Travel Model Framework

Characteristics of household vehicles can influence daily travel behavior and can vary greatly by vehicle type. Vehicle type is defined here as a combination of body type, fuel type, and age. Using data compiled primarily from the National Household Travel Survey, a multinomial logit model was developed to predict vehicle type based on characteristics of the household that owns the vehicle. The model was then implemented for the San Francisco Bay Area in the ActivitySim activity-based modeling framework and validated against observed data. The ActivitySim project’s goal is to create and maintain advanced, open-source, activity-based travel behavior modeling software, based on best software development practices, for distribution to the public, free of charge. A naïve 2030 scenario was analyzed to show model response to changes in the vehicle fleet.

Battery Electric Vehicles: Travel Characteristics of Early Adopters

Do U.S. households with battery electric vehicles (BEVs) drive less or more than U.S. households with internal combustion engine vehicles (ICEVs)? Answering this question is important to policymakers and transportation planners concerned with reducing vehicle miles traveled and the emissions of greenhouse gases from transportation. So far, this question has not been answered satisfactorily, possibly because of the relatively low number of EVs in the U.S. until recently, but also because of methodological issues. In this paper, we aim to fill this gap by analyzing data from the 2017 National Household Travel Survey (NHTS). We apply propensity score matching (PSM), a quasi-experimental method, to examine the differences in self-reported annual mileage and calculated daily mileage for various trip purposes among households with only BEVs (BEV-only), households with both BEVs and ICEVs (BEV+), and households without BEVs (non-BEV households). Our findings indicate that households with BEVs drive fewer annual miles than non-BEV households, but typically travel no less than they do for daily activities. This apparent discrepancy is likely due to taking fewer longer trips because the public charging infrastructure was still in its infancy in 2017, and its reliability was questionable. As technological progress is helping to overcome current battery limitations, policymakers may consider measures for fostering fast charging technologies while pondering new measures to fund both the charging infrastructure and the road network.

Are immigrants migrating away from transit? Immigrant transit use trends in California

While patronage on most public transit systems around the world remains modestly to significantly depressed post-pandemic, transit ridership had mostly been declining across the US since 2014. Advocates, practitioners, and scholars have offered a range of explanations for pre-pandemic patronage decline. This analysis examines the demand-side of this phenomenon by focusing on high-propensity transit riders, and, in particular, immigrants in the most populous US state: California. Drawing on the California add-on to the 2009 and 2017 National Household Travel Surveys, we evaluate changes in transit use among Hispanic and Asian immigrants. We find that during the study period, transit trip frequency of Hispanic immigrants declined, converging toward the ridership patterns of US-born Hispanics. While this trend was largely the result of rising incomes, increased vehicle ownership, and lower residential densities among non-US-born Hispanics, all else equal, Hispanic immigrants continued to use transit more than their US-born counterparts. By contrast, transit trip making by Asian immigrants diverged from that of US-born Asians. Notably, this divergence was not the result of declining per-person trip making by Asian immigrants, which was relatively stable during the study period. Instead, the growing gap was due to increases in transit trip frequency by US-born Asians.

Do low-income households walk and cycle to reduce their transport costs? Insights from the 2017 U.S. National Household Travel Survey

Low-income populations are disadvantaged in a car-dependent society despite car ownership and could be using walking and cycling to reduce their travel costs. This study explores how low-income households with and without cars living in various geographies disproportionately use walking and cycling to save money in comparison to higher-income households. Data from the 2017 National Household Travel Survey is used to investigate the variation in walking and cycling behavior among three groups of households: (1) carless low-income households, (2) low-income households with cars, and (3) higher-income households. Generalized ordered logistic regression models are estimated to examine how the probability of using active travel to save money varies by household type, location (urban, suburban, rural) and other socioeconomic attributes. We find that low-income households are more likely to walk or cycle to save money on transportation compared to higher-income households. Carless low-income households present a higher probability to use active travel to decrease travel costs in comparison to car-owning low-income households. Our results also indicate that on average, urban residents are more likely to travel actively to reduce expenses compared to suburban and rural residents. The lowest spatial variation is found for carless low-income households, demonstrating their higher disadvantage compared to those with cars. Low-income people of color are more likely to use active travel to save money while being a female, older, or having children are attributes associated with a lower probability to use active travel to reduce travel expenses in low-income households.

Trends and determinants of the mobility of older adults in the USA and Germany, 2001–2017

Using the 2001 and 2017 National Household Travel Surveys (NHTS) for the USA and the 2002 and 2017 Mobility in Germany Surveys (MiD) for Germany, this paper examines differences between the two countries in the travel behavior of older adults and to what extent it has changed between 2001/2002 and 2017. We use five mobility indicators to portray the travel behavior of older adults: whether or not a trip is made at all on the survey day (i.e., mobile or not); number of trips per day for mobile individuals; km of travel per day for mobile individuals; modal distribution of trips; and trip purpose. In analyzing the travel behavior of older adults, we grouped them into 5-year age categories: from 60–64, 65–69, 70–74, 75–79, and then 80+ years. In addition to a descriptive, graphical analysis, we employ multi-level multiple regression analysis to examine factors that may affect the mobility of older adults.Mobility levels generally declined with increasing age in both countries in both years, but the declines were gradual, except for a steep drop for the 80+ age group. Mobility levels in 2001 were generally higher for all age groups in the USA than in Germany, especially for older adults. By 2017, however, the gap between the two countries diminished because mobility increased more for all age groups in Germany than in the USA from 2001/2002 to 2017. Indeed, Germans of all age groups in 2017 were more likely than Americans to make at least one trip during their travel day.The largest increases in mobility in both countries were for the oldest age categories (75–79 and 80+). From 2002 to 2017, the share of car trips among older adults in Germany rose considerably, while it fell slightly in the USA from 2001 to 2017. Nevertheless, car dependence among the oldest adults in 2017 was still twice as high in the USA as in Germany. One reason for Germany’s lower modal share of driving is that the transport system in Germany offers far more attractive alternatives to car use than in the USA, while policies at every government level make car ownership, parking and use more expensive and more difficult than in the USA.

Determining causality in travel mode choice

This article presents one of the pioneering studies on causal modeling in travel mode choice decision-making using causal discovery algorithms. These models are a major advancement from conventional correlation-based techniques. We propose a novel methodology that combines causal discovery with structural equation modeling (SEM). This modeling approach overcomes some of the limitations of SEM by combining the strengths of both causal discovery and SEM. Causal discovery algorithms determine causal graphs from observational data and domain knowledge, and SEMs estimate direct causal effects and test the performance of causal discovery algorithms. In this study, we test four causal discovery algorithms: Peter-Clark (PC), Fast Causal Inference (FCI), Fast Greedy Equivalence Search (FGES), and Direct Linear Non-Gaussian Acyclic Models (DirectLiNGAM). The results show that DirectLiNGAM based SEM model best captures causality in mode choice behavior. It passes several goodness-of-fit tests, including Root Mean Square Error of Approximation (RMSEA) and Goodness-of-Fit Index (GFI), and it achieves the lowest Bayesian Information Criterion (BIC) value. The analyses are conducted on data collected from the 2017 National Household Travel Survey in the New York Metropolitan area.

Impacts of active mobility on individual health mediated by physical activities

Active mobility, encompassing walking and cycling for transportation, is a potential solution to health issues arising from inadequate physical activity in modern society. However, the extent of active mobility's impact on individual physical activity levels, and its association with health as mediated by physical activities, is not fully quantified. This study aims to clarify the direct relationship between active mobility usage and individual health, as well as the indirect relationship mediated by physical activity, with a focus on varying levels of physical activity intensity. Utilizing data from the 2017 U.S. National Household Travel Survey (NHTS), we employed Poisson regression to predict active mobility usage based on socio-demographic and household socio-economic characteristics. A Structural Equation Model (SEM) was then used to investigate the direct and indirect effects of active mobility on individual health, mediated by physical activity. We further segmented individuals according to their intensity of physical activity to examine how such effect differs between different levels of physical activity. The study demonstrates that active mobility usage positively correlates with both the amount and intensity of physical activity. The effect of active mobility on individual health includes a direct positive effect (29% for intensity, 67.7% for amount) and an indirect effect mediated by physical activity (71% for intensity, 32.3% for amount). Notably, the mediation effect of active mobility on health is more substantial in the context of vigorous physical activities compared to light or moderate activities. Our findings reveal a significant positive influence of active mobility on individual health, encompassing both direct and indirect effects mediated by physical activities. These results quantitatively underscore the health benefits of active mobility and suggest the importance of promoting active mobility as a strategy to improve public health.

Are Generation Z Less Car-Centric Than Millennials? A Nationwide Analysis Through the Lens of Youth Licensing

Are young Americans becoming less car-centric? The National Household Travel Survey (NHTS) data reveals a noticeable decline in the rate of driver's license acquisition among teens aged 16–20 years, with 65.4 % holding a driver's license in 2017. The percentage is 8.3 % and 8.1 % lower than in 2001 and 2009, respectively. This research compares the differences in driver's license acquisition between Millennials (teens in 2009) and their succeeding generation – Generation Z (teens in 2017) during late adolescence. It also investigates the factors that influence a teen's decision to hold a driver's license. Findings suggest that the dropped licensing rates between the two survey years can be partially explained by a generational shift in attitudes and cultural changes. Some traits of Generation Z, such as making more educational trips and growing up in a digital world, may significantly influence their decisions about obtaining a driver's license. To further explore whether Generation Z will drive less than Millennials once getting a driver's license, a multivariate analysis is conducted on licensed teens in 2009 and 2017, focusing on their driving distances on the survey day. Notably, this study does not find a significant difference between teens aged 18–20 years from the two generations. This research draws implications for planners, practitioners, and policymakers to proactively respond to the possible consequences of changing American car culture.

Shopping trips and deliveries then and now: How the behavioral shift differs by income level and urban size

Online shopping has significantly increased for the past decade. As e-commerce grows and evolves since its first appearance, consumers’ shopping behaviors also change, leading to the co-evolution of people’s shopping trips and urban freight deliveries. This co-evolution has important impacts on traffic, vehicle-miles-traveled (VMT) and parking demand in the short term, and is expected to influence residential choice and land-use patterns in the long term. However, the existing studies have not examined how the relationship among shopping channels changed over time. To address this knowledge gap, this research investigated how the complex relationship between delivery and in-store shopping trips changed from when e-commerce was relatively small to a stabilized stage, i.e., from 2009 to 2017, using the National Household Travel Survey (NHTS) data. The results from path analyses showed that the relationships among delivery, non-motorized shopping trip, and motorized shopping trip significantly shifted during an eight-year period. Not only the relationship among shopping channels but also the impacts of socio-demographic factors on shopping had changed over time. Furthermore, the behavioral shift varied by income level and urban size. This research is the first attempt investigating a heterogeneous historical trend from the transportation perspective. An understanding of how the relationship has been shifted and affected by income and urban environments will provide valuable insights into proactively planning and establishing freight demand management as well as addressing the inequality issue. The lessons learned will also be of assistance to better understand how consumers will respond to the emerging freight delivery service forms (e.g., prepared meals and grocery delivery) and their impacts on both passenger and freight transportation systems.

Travel Behavior of Older Adults in the USA, 2001-2017

This study examines trends in the mobility of older adults in the USA between the years 2001 and 2017 and how mobility varies among 5-year subgroups of older adults from 55 to 80+ years. Based on data from the 2001 and 2017 National Household Travel Surveys (NHTS), we use descriptive, graphical, and multilevel regression analysis to detect differences in travel behavior and its determinants. Mobility tends to decrease with age, even after controlling for density, gender, race, income, employment, medical condition, car ownership, and having a driver’s license. Compared to younger age groups, older adults were more likely not to make a trip during the travel day. Among mobile individuals, older adults made more trips but traveled fewer miles per day. Mobility declined for each of the successively older 5-year age categories. The 80+ age group had, by far, the lowest levels of mobility. Older age groups increased their mobility relative to younger age groups between 2001 and 2017.

Quantifying active travel among people with disabilities in the US

BackgroundPeople with disabilities have higher rates of physical inactivity than people without. Active travel (e.g., walking/wheeling to nearby destinations or transit) is a recommended approach to increasing total physical activity (PA) but limited research has examined active travel among people with disabilities. ObjectiveTo describe active travel among a nationally representative sample of people with disabilities, analyze variation between sub-groups, and examine factors associated with active travel. MethodsUsing the 2017 National Household Travel Survey, our cross-sectional analysis summarized counts and duration of walking/wheeling trips for people with four different types of disabilities. We examined which factors were associated with doing any active travel and the duration of active travel, using zero inflated negative binomial regression models. ResultsOur analysis identified that 14.55% of people with disabilities took a mean of 2.56 (95%CI = 2.42–2.69) walking/wheeling trips per day. Compared to non-active travelers, a higher proportion of active travelers were low-income, lived alone, had no-vehicle, and were Black or Hispanic. For active travelers, daily walking minutes, on average, were 46.41 (95%CI = 40.25–52.57) among people who used ambulatory devices, 41.55 (95%CI = 24.61–58.49) among people who were blind/low-vision, 39.93 (95%CI = 35.41–44.45) among people who used no device, and 29.58 (95%CI = 23.53–35.64) among people who used chair devices. Our analysis identified individual, household, and community factors associated with the likelihood and duration of walking/wheeling for travel and variation across disability types. ConclusionsUnderstanding the multiple identities of active travelers with disabilities can inform walking/wheeling intervention strategies. Infrastructure improvements that support less reliance on automobiles could increase active travel among people with disabilities.