National Household Travel Survey Data Research Articles

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.

A case study of checking national household travel survey data with machine learning

In recent years, machine learning techniques have been increasingly tested and applied to physically collected data to optimize the processes. In this paper, machine learning is used to check travel survey data of the German Mobility Panel (MOP). In the MOP, verified and raw data have been available for several decades, on which algorithms can learn the practices of human checking routines. By using machine learning, the algorithm is expected to learn the checking patterns from the past and thus support the data checking of new datasets. To this aim, several algorithms are applied and tested. The presented model framework supports the identification of blatant deficits in the reports at the individual and trip levels. The neural network (NN) shows the most promising results as it decreases the number of data samples checked. The checking effort can be reduced by 20.4 % at the individual trip level. This work shows that machine learning can support the data checking process in the MOP at various levels, thus leading to significant time reduction.

Exploring the endogenous effects among car dependency, work arrangement choice, and daily travel using the 2017 NHTS data

The paper presents an effort in investigating the cause-effect relationships among telecommuting, car dependency, and trip making behavior. Data from the 2017 National Household Travel Survey (NHTS) was used to develop a structural equations model (with latent constructs) to examine the endogenous effects between the decision to telework, the degree of car dependency, and the number of daily trips. Using confirmatory factor analysis, car dependency was measured by vehicle ownership, annual mileage, and car use frequency. The model provided interesting insights. Car dependency had a negative (both direct and total) impact on telework, an inference that confirms the role of car dependency as a resistant factor against alternative work arrangements and potentially any other new tech-based policies. In the opposite direction and surprisingly, telework encouraged car dependency, which might indicate that teleworkers tended to utilize the relaxed temporal-spatial constraints for non-work trips, and they were unlikely to give up private vehicle ownership simply because of their option to telecommute. Second, number of daily trips tended to increase the probability to telework, confirming the positive endogenous role of daily activity patterns on work arrangement decisions in a daily framework. Lastly, the significant negative impact of telework on daily trips was positively mediated through car dependency, leading to a smaller negative total impact on daily trips. Our study confirms that the association between telework and trip generation is quite a complex and multi-faceted relationship, leaving the argument regarding whether or not telework increases the number of daily trips as a debatable topic that calls for further exploration.

Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities

Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless EV charging systems are considered as an alternative charging method for EVs, this can affect the load of a rapid EV charging station in terms of time and magnitude. Hence, this paper presents a probabilistic framework for estimating the arrival rate of EVs at an EV rapid charging station, considering the availability of on-road wireless charging systems as an alternative charging method. The proposed model incorporates an Electric Vehicle Decision Tree that predicts the times when EVs require rapid charging based on realistic transportation data. A Monte Carlo simulation approach is used to capture uncertainties in EV user decisions regarding charging types. A queuing model is then developed to estimate the charging load for multiple EVs at the charging station, with and without the consideration of on-road EV wireless charging systems. A case study and simulation results considering a 32-bus distribution system and the US National Household Travel Survey (NHTS) data are presented and discussed to demonstrate the impact of on-road wireless EV charging on the loads of an rapid EV charging station. It is observed that having on-road wireless EV charging as complementary charging to EV charging stations helps to significantly reduce the peak load of the charging station, which improves the power system capacity and defers the need for system upgrades.

Factors influencing mode choice of adults with travel-limiting disability

IntroductionDespite the plethora of research devoted to analyzing the impact of disability on travel behavior, not enough studies have investigated the varying impact of social and environmental factors on the mode choice of people with disabilities that restrict their ability to use transportation modes efficiently. This research gap can be addressed by investigating the factors influencing the mode choice behavior of people with travel-limiting disabilities, which can inform the development of accessible and sustainable transportation systems. Additionally, such studies can provide insights into the social and economic barriers faced by this population group, which can help policymakers to promote social inclusion and equity. MethodThis study utilized a Random Parameters Logit model to identify the individual, trip, and environmental factors that influence mode selection among people with travel-limiting disabilities. Using the 2017 National Household Travel Survey data for New York State, which included information on respondents with travel-limiting disabilities, the analysis focused on a sample of 8,016 people. In addition, climate data from the National Oceanic and Atmospheric Administration were integrated as additional explanatory variables in the modeling process. ResultsThe results revealed that people with disabilities may be inclined to travel longer distances walking in the absence of suitable accommodation facilities for other transportation modes. Furthermore, people were less inclined to walk during summer and winter, indicating a need to consider weather conditions as a significant determinant of mode choice. Moreover, low-income people with disabilities were more likely to rely on public transport or walking. ConclusionBased on this study's findings, transportation agencies could design infrastructure and plan for future expansions that is more inclusive and accessible, thus catering to the mobility needs of people with travel-limiting disabilities.

Deployment priority of public charging speeds for increasing battery electric vehicle usability

To inform charger deployment decisions, this paper aims to understand the potential utilization and deployment priority for public charging infrastructure. A data-driven Cumulative Public Recharging (CPR) model is developed to explore the travel patterns by using 2017 National Household Travel Survey data. Given the daily trip sequence, trip distance, and dwell times, the study examines the daily expected driving range and BEV feasibility under different charging speeds, battery capacity, and charging behaviors. The results suggest that more advanced public chargers increase the daily expected driving range. Home charging is sufficient for most daily short-distance trips while public chargers are still needed for medium- and long-distance trips. Extreme fast charging (xFC) may not be necessary for people with home charging but could be more useful for people without home charging and for urgent charging. xFC becomes even less important to drivers with longer BEV ranges, a finding that contradicts conventional thinking.

Forecast Performance of Metropolitan Trip Generation Models Statistically Updated with US National Household Travel Survey Data

Forecast Performance of Metropolitan Trip Generation Models Statistically Updated with US National Household Travel Survey Data

Emissions Reductions from Electrifying High-Mileage Vehicles

This paper evaluates a strategy that would target sales of internal combustion vehicles driven at high annual mileage for displacement by electric vehicles at the time of initial sale. Using the 2017 National Household Travel Survey data, we observe that the top 20% of light duty vehicles by kilometers traveled generate 46% of the annual greenhouse gas emissions. Displacing the sale of a combustion engine vehicle in the top mileage quintile with an electric vehicle would reduce annual greenhouse gas emissions and certain criteria pollutants by more than 15 times as much as displacing a vehicle in the bottom mileage quintile.

Assessment of Distribution System Margins Considering Battery Swapping Stations

Penetration of electric vehicles (EVs) into the market is expected to be significant in the near future, leading to an significant increase in EV charging demand, and that will create a surge in the demand for electrical energy. In this context, there is a need to find intelligent and cost effective means to make better use of electricity resources, improve the system flexibility, and slow the growth in demand. Therefore, swapping EV batteries rather than traditionally charging them can serve as flexible sources to provide capacity support for the power distribution grid when they are charged during off-peak periods prior to their swapping at the station. This paper presents a novel mathematical optimization model to assess distribution system margins considering different EV charging infrastructures. The proposed model maximizes the distribution system margins while considering the flexibility of battery swapping station loads and distribution grid limitations. To demonstrate the effectiveness of the proposed model, simulation results that consider the National Household Travel Survey data and a 32-bus distribution system are reported and discussed. Unlike charging EV batteries, swapping them would not affect system margins during the peak hours.

Exploring Heterogeneous Structural Relationships Between E-Shopping, Local Accessibility, and Car-Based Travel: An Application of Enriched National Household Travel Survey Add-on Data

This study analyzes structural relationships between information and communication technology (ICT), e-shopping, local accessibility, and travel intensity in the U.S. state of Georgia. Beginning with the 2017 National Household Travel Survey (NHTS), we enriched the data by exploiting the add-on program. After obtaining contact information for the Georgia NHTS participants who expressed willingness to take a follow-up survey, we conducted another survey with those participants. In addition, we appended land use characteristics associated with respondents’ home locations. To test the hypothesis of possible heterogeneity in structural relationships, we applied a mixed structural equation model. We corroborated our hypotheses that the use of ICT has a positive impact on e-shopping frequency, whereas local accessibility reduces e-shopping frequency. In addition, we identified two meaningful segments exhibiting different structural relationships. The smaller segment (12.7%) showed complementary effects of e-shopping on car-based travel intensity, but local accessibility did not have a significant impact on car-based travel intensity. The dominant segment (87.3%) presented a negative impact of local accessibility but no meaningful effect of e-shopping frequency on car-based travel intensity. A post-hoc analysis revealed meaningful differences in demographics between the two classes. The study suggests that an assumption of homogeneous structural relationships between e-shopping and car-based travel intensity and between local accessibility and car-based travel intensity may not hold. The paper discusses benefits and practical issues concerning fusion of the NHTS data with other data sources. In addition, it stresses the potential of the NHTS add-on program and notes some recommendations.

Development and Application of a Rural Intercity Demand Model

This paper describes the development of a demand model to estimate ridership for rural intercity bus services in the United States. The need for such a model and the approach used in developing it are described. Two models were developed, one a regression equation calibrated on data from a survey of rural intercity services, and the other using a trip rate developed from National Household Travel Survey data. Both models are included in a toolkit that also includes user information and population data. The paper then compares the ridership predictions made using the model with actual experience on rural intercity routes in Washington State and illustrates how it can be used as part of a statewide assessment for Vermont. Conclusions about its applicability and directions for future research are presented.

A Taste for Transit? Analyzing Public Transit Use Trends among Youth

In the past decade, there has been much talk about a decline in driving among youth. This study examined whether this decline is associated with an increased reliance on public transit. To address this issue, 2001 and 2009 National Household Travel Survey (NHTS) data were used to analyze the relationship between age and transit use. Findings indicate that although young adults are more likely to ride transit than older adults, transit use among youth can be explained largely by (1) life cycle factors common among young people but unlikely to persist as they age, (2) higher levels of transit use among non-whites, who are disproportionately young, and (3) locational factors such as living in densely-developed neighborhoods that may or may not continue as young people age. Therefore, whereas transit habits established early in life may persist as young adults age, the data examined here suggest that such an outcome is far from assured.

Exploring the nonlinear effects of ridesharing on public transit usage: A case study of San Diego

The emergence of ridesharing services might complement or substitute public transit systems, leading to intricate relationships between the two services. However, limited studies focused on the nonlinear effects of ridesharing use frequency on public transit usage. Therefore, this paper investigated such nonlinear effects using the hierarchical negative binomial generalized additive model (HNBGAM), with the latest publicly available National Household Travel Survey (NHTS) dataset. The negative binomial and hierarchical negative binomial generalized linear models were also developed for comparison with the HNBGAM. The NHTS data involved travel information of 928 ridesharing users within 98 census tracts in San Diego. Two-level hierarchy (individual and census tract level) was constructed in the HNBGAM. In addition, the smooth function of the HNBGAM could help identify the nonlinear effects of ridesharing use frequencies on public transit usage. Demographic factors (age, gender, race, household size, etc.) and built environment factors (e.g., population density, worker density, percentage of rental houses, and house unit density) were also considered in the modeling process. The findings revealed a negligible impact on public transit usage for occasional ridesharing use (from one to eleven times per month), a complementary effect for regular ridesharing use (from eleven to thirty-two times per month), and a substitution effect for active ridesharing use (more than thirty-two times per month). Understanding such nonlinear relationships could help policymakers make more informed decisions to avoid the over-substitution of public transit usage and better complement the public transport system.

Interaction Between Information and Communication Technologies and Travel Behavior: Using Behavioral Data to Explore Correlates of the COVID-19 Pandemic

The COVID-19 pandemic has highlighted the importance of information and communication technologies (ICTs) in providing virtual engagement. Planners and engineers must determine whether cities will see reductions in travel demand, given the increasing use of ICTs. Notably, ICTs facilitate online shopping and working from home (WFH). Generally, online shopping may lead to fewer shopping trips; similarly, WFH may reduce work-related trips. However, more WFH has the potential to generate other non-work trips, including shopping trips. To find answers and explore interdependencies, this study integrates pre-pandemic behavioral data with during-pandemic travel data. In our framework, WFH and online shopping are considered together. By harnessing the pre-pandemic 2017 National Household Travel Survey data, this study jointly analyzes the relationships between shopping trips, online shopping, and WFH with a conditional mixed process model that can address unobserved endogeneity and selection bias. The results suggest that, before the pandemic, online shopping was associated with fewer in-person shopping trips while WFH was associated with more shopping trips. The role of socio-demographic, locational, and travel-related factors is also explored. The during-pandemic data and analysis capture how COVID-19 affected travel behavior. Results show that the relationships among the key variables found in the pre-pandemic data are similar but differ in magnitude from during the pandemic. WFH increased from 12% to 61% during COVID-19, admittedly an unusual situation. In the next “new normal,” planners may improve travel demand models by treating WFH explicitly as an alternative to traveling to work in the trip generation and time of day models.

Electric vehicle market potential and associated energy and emissions reduction benefits

In this paper, a methodological framework is proposed to assess the potential of electric vehicle (EV) penetration and corresponding reduction in energy use and emissions in Georgia, U.S., using 2017 National Household Travel Survey data. A two-phase, data-driven model assesses the potential for household purchases of EVs and the assignment of EVs to household trips. Households sharing the highest similarities are selected as candidates for EV purchases, with household trips identified as EV-amenable or not. Potential EV-purchasing families were also matched to specific EV makes and models. Energy use, greenhouse gas emissions, and criteria air pollutants were analyzed and compared for all original trips and for those trips that shifted to EVs. By comparing against two traditional averaging methods, the framework demonstrates an advancement that helps to avoid the overestimation of EV benefits. By integrating household-level demographics and trip-level attributes from open-source travel survey data in EV adoption and trip assignment, this paper demonstrates the benefits associated with EV adoption (a potential 45% reduction in energy consumption and 30% reduction in greenhouse gas emissions), moreover, the proposed methodology could serve as an innovative framework that is scalable and transferable to predict the future market penetration and actual on-road EV activities under various contexts.

Exploring the role of ride-hailing in trip chains

Ride-hailing can potentially provide a variety of benefits to individuals who need to chain several activities together within a single trip chain, relative to other travel modes. Using household travel diary/survey data, the goal of this study is to assess the role ride-hailing currently plays within trip chains. Specifically, the study aims to determine, within trip chains, who uses ride-hailing services, for what trip/activity purposes, and to/from what types of areas, as well as the characteristics of trip chains that involve ride-hailing segments. To meet these objectives, the study estimates a binary logit model using 2017 National Household Travel Survey data, where the dependent variable denotes the inclusion of at least one ride-hailing trip within a trip chain. Similar to the non-trip-chaining ride-hailing literature, this study indicates that trip chains with ride-hailing legs are positively associated with travelers who are younger, live in high-income households, frequently use transit, and reside in high-density areas. However, this study includes novel findings indicating statistically significant relationships between ride-hailing and trip chains that end in healthcare and social/recreational activities. Moreover, trip chains with ride-hailing tend to have fewer stops and longer activity durations than trip chains without ride-hailing. This study also includes nested logit choice models, wherein the dependent variable denotes the primary mode (ride-hailing, transit, personal vehicle, or non-motorized transport) of a trip chain. These model results provide additional insights into the role of ride-hailing within trip chains, as they allow for cross-mode comparisons. The paper discusses the potential transportation planning and policy implications of the model results as well as future research directions.

Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017.

Despite the rapid growth of online shopping and research interest in the relationship between online and in-store shopping, national-level modeling and investigation of the demand for online shopping with a prediction focus remain limited in the literature. This paper differs from prior work and leverages two recent releases of the U.S. National Household Travel Survey (NHTS) data for 2009 and 2017 to develop machine learning (ML) models, specifically gradient boosting machine (GBM), for predicting household-level online shopping purchases. The NHTS data allow for not only conducting nationwide investigation but also at the level of households, which is more appropriate than at the individual level given the connected consumption and shopping needs of members in a household. We follow a systematic procedure for model development including employing Recursive Feature Elimination algorithm to select input variables (features) in order to reduce the risk of model overfitting and increase model explainability. Among several ML models, GBM is found to yield the best prediction accuracy. Extensive post-modeling investigation is conducted in a comparative manner between 2009 and 2017, including quantifying the importance of each input variable in predicting online shopping demand, and characterizing value-dependent relationships between demand and the input variables. In doing so, two latest advances in machine learning techniques, namely Shapley value-based feature importance and Accumulated Local Effects plots, are adopted to overcome inherent drawbacks of the popular techniques in current ML modeling. The modeling and investigation are performed at the national level, with a number of findings obtained. The models developed and insights gained can be used for online shopping-related freight demand generation and may also be considered for evaluating the potential impact of relevant policies on online shopping demand.

How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE

AbstractMachine learning (ML) is being used regularly in many different fields. This paper compares traditional econometric methods that have better explanations of data analysis to ML methods, focusing on predicting, understanding and unpacking ML methods which have higher prediction accuracies of four key transport‐planning variables: household vehicle‐miles traveled (continuous variable), household vehicle ownership (count variable), mode choice (categorical variable), and land use change (categorical variable with strong spatial interactions). Here, the results of ten ML methods are compared to methods of ordinary least squares (OLS), multinomial logit (MNL), negative binomial and spatial auto‐regressive (SAR). The U.S.’s 2017 National Household Travel Survey and land use data sets from the Dallas‐Ft. Worth region of Texas are used. Results suggest traditional econometric methods work pretty well on the more continuous responses (VMT and vehicle ownership), but the random forest (RF), gradient boosting decision trees (GBDT), and extreme gradient boosting (XGBoost) methods delivered the best results, though the RF model required 30 to almost 60 times more computing time than XGBoost and GBDT methods. The RF, GBDT, XGBoost, light gradient boosting method (lightGBM), and catboost offer better results than other methods for the two “classification” cases, with lightGBM being the most time‐efficient. Importantly, ML methods captured the plateauing effect modelers may expect when extrapolating covariate effects.

Changes in the travel patterns of older Americans with medical conditions: A comparison of 2001 and 2017 NHTS data

The older population in the U.S. is increasing and the physiological, cognitive, and psychological changes of aging can potentially pose mobility barriers for them. A medical condition further impedes travel opportunities and hinders continued engagement in civic and social life, adversely impacting their quality of life. This study uses the 2001 and 2017 National Household Travel Survey (NHTS) data and analyzes the socio-demographic, attitudinal, and travel characteristics associated with older population long-distance travel patterns to support policy changes that address their emerging travel needs. The study results find a shift in trend in the mode preference of the new generation of older people with a medical condition. They are less likely to drive a car and prefer riding a bus or some other transport modes. They are also more likely to travel more over the weekend and evening hours. The older individuals are also more likely to travel long distances for work/business, religious trips, visits to family, friends, and relatives, but not for shopping, medical, and recreational purposes. Income and education levels of the older population with a medical condition directly impact their long-distance travel and are likely to increase in the future. The new generation of older people with a medical condition (i.e., 2017 data) are less likely to self-regulate travel by limiting or giving up driving and are more likely to be self-dependent by refusing to ask for rides or use special transportation. As a result, more older drivers with medical conditions can be expected to drive on the roads in the future adding to their road safety risks as well as those of others. This warrants transport policy changes to support safer mobility options for older people for their continued active community life and well-being.

Equity of access to Uber's wheelchair accessible service

Ridesourcing service providers have expanded rapidly in recent years, but accessibility for people with disabilities is an ongoing issue. UberWAV was launched in September 2015, providing wheelchair accessible vehicles equipped with a rear-entry ramp, winch, and restraints for riders who travel with a wheeled mobility aid. The objectives of this study were 1) to assess the equity performance of UberWAV by itself and in comparison to UberX by investigating the temporal and spatial distributions of the availability and accessibility of each service, and 2) to compare the availability, wait time, and price of UberWAV in Portland to the requirements in the city code. Demand-weighted probability of availability (WPA) and demand-weighted estimated time of arrival (WETA) were calculated for each service using Application Programming Interface (API) data extracted at 71 gridded locations in Portland, Oregon in 2018 and U.S. National Household Travel Survey data. WPA and WETA were then regressed over sociodemographic variables, controlling for spatial correlation, to investigate systematic patterns in access by neighborhood. Results indicate large disparities in availability between the WAV and standard Uber services. WPA of UberX was consistently over 99%, while WPA for UberWAV averaged 62% with substantial fluctuations by time of day. Average WETA for UberWAV was 18.8 min, 4.5 times higher than for UberX, and again had greater temporal variability. UberWAV met the service requirements in the Portland city code for availability and wait time under 30 min just 58% of the time (demand-weighted). While UberWAV access was substantially poorer than for UberX, the access was not inequitably distributed with regard to neighborhood income or racial composition. UberWAV was more accessible in areas where a higher percentage of people with ambulatory disabilities lived. A larger fleet of UberWAV vehicles would be needed, particularly through the middle of the day, if Uber is to provide equal access for people using a wheelchair and to meet the requirements in the Portland city code.