California Household Travel Survey Research Articles

Heterogeneity in the relationship between biking and the built environment

Bicycling is an environmentally friendly, healthy, and affordable mode of transportation that is viable for short-distance trips. Urban planners, public health advocates, and others are therefore looking for strategies to promote more bicycling, including improvements to the built environment that make bicycling more attractive. This study presents an analysis of how key built environment characteristics relate to bicycling frequency based on a large sample from the 2012 California Household Travel Survey (California Department of Transportation, 2012) and detailed built environment data. The built environment characteristics we explore include residential and intersection density at anchor locations (home, work, school), green space, job access, land-use mix, and bicycle infrastructure availability. Analyses are conducted separately for three distinct demographic groups: school-age children, employed adults, and adults who are not employed. The key conclusion from this work is that the relationship between bicycling and some built environment characteristics varies between types of people — most dramatically between adults and children. To develop targeted policies with scarce resources, local policymakers need specific guidance as to which investments and policy changes will be most effective for creating “bikeable” neighborhoods. Our work indicates that the answer depends — at least in part — on who these bikeable neighborhoods are meant to serve.

The effect of parenthood on travel behavior: Evidence from the California Household Travel Survey

Literature suggests that young children have a significant influence on activity patterns and time-use of adult men and women in dual-earner households. The resultant impact on travel behavior, however, remains largely unexplored. In this study, we use the 2012–13 California Household Travel Survey to compare daily (weekday) travel behavior of adult men and women belonging to dual-earner heterosexual couple households without children, with their adult counterparts in dual-earner heterosexual nuclear households with one or more young (aged 15 years or less) children living in urban California. We find that the presence of young children is, on average, associated with relatively higher auto use, and lower levels of physically active travel (i.e., walking and bicycling) and public transit use. Specifically, parents of school-age (6–15 years) children, without other small (5 years or less) children, are found to engage in significantly more auto use than childless couples. The likelihood of engaging in 20 min or more of active travel per day falls as couples transition to parenthood, and drops further as small children turn school-age. The likelihood of making at least one transit trip per day follows a similar pattern. We also find that the negative impact of young children on average, and school-age children in particular, on adults’ active travel is significantly greater for men than women. Additionally, we identify factors that can help reduce gender inequality in auto use and active travel within households with one or more young children. This study enhances our understanding of travel behavior variations across household types in cities, and over the life courses of individuals. Planning and policy implications are discussed.

Car-deficit households: determinants and implications for household travel in the U.S.

In the U.S., households with less than one car per driver (auto-deficit households) are more than twice as common as zero-vehicle households. Yet we know very little about these households and their travel behavior. In this study, therefore, we examine whether car deficits, like carlessness, are largely a result of financial constraint or of other factors such as built environment characteristics, household structure, or household resources. We then analyze the mobility outcomes of car-deficit households compared to the severely restricted mobility of carless households and the largely uninhibited movement of fully-equipped households, households with at least one car per driver. Data from the California Household Travel Survey show that car-deficit households are different than fully-equipped households. They have different household characteristics, travel less, and are more likely to use public transit. While many auto-deficit households have incomes that presumably enable them to successfully manage with fewer cars than adults, low-income auto-deficit households are—by definition—income constrained. Our analysis suggests that low-income car-deficit households manage their travel needs by carefully negotiating the use of household vehicles. In so doing, they travel far more than carless households and use their household vehicles almost as much as low-income households with at least one car per driver. These results suggest that the mobility benefits of having at least one car per driver are more limited than we had anticipated. Results also indicate the importance of transportation and employment programs to ease the potential difficulties associated with sharing cars among household drivers.

Shared school transportation: determinants of carpooling as children’s school travel mode in California

Carpooling has potential as an alternative mode of school transportation along with other viable options, especially at a time when technology continues to increase our reliance upon shared mobility. Unfortunately, our knowledge of carpooling as a school travel mode is very limited. The purpose of this paper is to fill this gap. Using a multinomial logit model, this study presents an analysis of data from the 2012 California Household Travel Survey to assess the effects of various factors, such as trip characteristics, child characteristics, parental or caregiver’s characteristics, household characteristics, and spatial variables on choosing carpooling as a school travel mode. The findings of the study indicate that travel distance is one of the major determinants of carpooling, suggesting that children are more likely to carpool to school as travel distance from home to school increases. The analysis shows that a higher income two-parent two-earner family with a 5–15-year-old female schoolchild is more likely to use carpooling for school trips when compared to other modes of transportation. Parental/caregiver characteristics are also found to be important, as children from households with young, female, higher educated heads are more likely to carpool to school. Results of spatial variables suggest that families living in neighborhoods with higher numbers of schoolchildren are also more likely to carpool. The empirical evidence presented in this study provides useful insight to school districts, policymakers, and other transportation related entities in identifying potential target groups to whom this travel mode could be presented.

Navigation Application Programming Interface Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles

The green-routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation application programming interface (API), route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). Navigation APIs, such as Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin–destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption models are built by assigning travel from many global positioning system driving traces to the links in TomTom MultiNet and road grade data from the U.S. Geological Survey elevation data set. Fuel consumption on a link is computed by the proposed model. This paper envisions two kinds of applications: (1) identifying alternate routes that save fuel, and (2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey global positioning system trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and travel time due to choosing different routes is also examined for both powertrains.

Does Investing in Rail Transit Benefit the Poor? A Comparative Study of Rail and Bus Travel by Low-Income Households in the California Household Travel Survey

This paper addresses academic discourse that critiques urban rail transit projects for their regressive impacts on the poor and proposes bus funding as a more equitable investment for urban transit agencies. The author analyzed data from the 2012 California Household Travel Survey on transit trips in Los Angeles County. The author cross-tabulated data on the modal breakdown of transit trips by household income category and on the breakdown of household income associated with trips by bus and rail transit modes. The author also comparatively evaluated the speed of trips (as a ratio of miles per hour) taken by rail and by bus by low-income households in the county. The author found convincing evidence that, on average, trips low-income households made by rail transit covered a greater distance per hour than trips taken by bus transit, but that trips made on the county’s bus rapid transit services with dedicated rights-of-way had a higher mean speed than those taken by rail. Moreover, the mode and income cross-tabulations indicate that rail transit projects only partially serve low-income households’ travel needs. To the extent that equitable transit planning entails minimizing the disparities in access, both rail and bus rapid transit projects can advance social justice if they are targeted at corridors where they can serve travel demand by low-income, transit dependent households.

The Joint Effects of Income, Vehicle Technology, and Rail Transit Access on Greenhouse Gas Emissions

This paper examines the relationship between income, vehicle miles traveled (VMT), and greenhouse gas (GHG) emissions for households with varying access to rail transit in four metropolitan areas—Los Angeles, the San Francisco Bay Area, San Diego, and Sacramento—using data from the 2010–2012 California Household Travel Survey. Daily vehicle GHG emissions are calculated using the California Air Resources Board’s 2014 EMFAC (emission factors) model. Two Tobit regression models are used to predict daily VMT and GHG by income, rail transit access (within or outside 0.5 miles of a rail transit station in Los Angeles and the Bay Area, and linear distance to rail in San Diego and Sacramento), and metropolitan area. Comparing predicted VMT and GHG emissions levels, this paper concludes that predicted VMT and GHG emission patterns for rail access vary across metropolitan areas in ways that may be related to the age and connectivity of the areas’ rail systems. The results also show that differences in household VMT due to rail access do not scale proportionally to differences in GHG emissions. Regardless, the fact that GHG emissions are lower near rail transit for virtually all income levels in this study implies environmental benefits from expanding rail transit systems, as defined in this paper.

A study on opportune reduction in greenhouse gas emissions via adoption of electric drive vehicles in light duty vehicle fleets

Electrified powertrain vehicles have the potential to bring about significant reductions in greenhouse gas (GHG) emissions in light-duty vehicle fleets as they replace conventional vehicles. Compared to electrified powertrains, a conventional vehicle (CV), whose driving power only comes from an internal combustion engine (ICE), is in many ways disadvantaged when in driven in conditions that involve frequent stops or idling. As a result, the amount of achievable reduction in GHG by replacing a CV with one that has an electrified powertrain can vary significantly depending on the driving patterns in which the vehicle is expected to be driven in. This work analyzes more than 65 thousand real-world trips from California household travel survey (CHTS) in order to highlight and quantify the differential benefit in opportune reduction of GHG emissions. Equivalent GHG emissions are estimated using a public-domain fuel economy simulations software for each trip if it was done on a mid-sized CV, a matching hybrid-electric vehicle (HEV), two different all-electric range (AER) plug-in hybrid electric vehicles (PHEVs), as well as a pure battery-electric vehicle (BEV). A clustering approach is used to discern sub-populations of vehicle samples in CHTS data set into four vehicle groups, ordered from least to most city-like driving patterns. Results show that replacing a CV in the group with city-like driving could reduce the GHG emissions by appreciably larger amounts (approx. two to three times) than in the group with less city-like driving.

A Latent Class Pattern Recognition and Data Quality Assessment of Non-Commute Long-Distance Travel in California

This study analyzes 8-week long-distance travel records from the California Household Travel Survey for completeness and identifies general types of non-commute long-distance tours using Latent Class Analysis. Likely due to the difficulty of gathering data of this kind, there has been relatively limited study of non-commute long-distance travel, despite the substantial contribution to many households’ greenhouse gas emissions and travel expenses. The California Household Travel Survey includes a valuable long-distance 8-week travel dataset, but this study identifies several possible shortcomings in the dataset. Of particular importance is a severe underreporting of shorter trips, which may result from a mix of respondent forgetfulness and survey fatigue. Despite the issues with the data, latent class cluster analysis was able to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour, and they are done by different types of households. The method used here to identify the typology of long-distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings.

Tour-Based Path Analysis of Long-Distance Non-Commute Travel Behavior in California

Data of long-distance tours by each household from an 8-week California Household Travel Survey travel log are analyzed in this paper. Each tour record contains summary data from a single-day diary, household sociodemographic information, and place of residence characteristics. Each tour contains a main trip, selected tours with a main trip that is not a commute trip, and added destination descriptors from Foursquare. Path analysis is used on three censored variables (tour miles by air, miles driving, and miles by public transportation) and two categorical variables (main trip tour purpose) and number of overnight stays. Annual household income is a major determinant of air travel and multiple overnight stays. Moreover, travel by air is more likely to be in large dense destinations with high attractiveness ratings. In contrast, car ownership is a major determinant of more mileage accrued driving a car to urban environments. In terms of main trip purpose, work and shopping are not associated with overnight stays and vacation/sightseeing is the purpose associated with longer overnight stays. Moreover, California destinations are more likely to be in tours completed within a day. It was also found that vacation, sightseeing, and leisure trips are more likely to be combined with other vacation and sightseeing trips. In contrast, business and combined business and leisure trips are less likely to be combined with vacation trips. There were also substantial and significant differences in long-distance tour behavior among residents of urban versus rural environments.

Travel Behavior Classification: An Approach with Social Network and Deep Learning

Uncovering human travel behavior is crucial for not only travel demand analysis but also ride-sharing opportunities. To group similar travelers, this paper develops a deep-learning-based approach to classify travelers’ behaviors given their trip characteristics, including time of day and day of week for trips, travel modes, previous trip purposes, personal demographics, and nearby place categories of trip ends. This study first examines the dataset of California Household Travel Survey (CHTS) between the years 2012 and 2013. After preprocessing and exploring the raw data, an activity matrix is constructed for each participant. The Jaccard similarity coefficient is employed to calculate matrix similarities between each pair of individuals. Moreover, given matrix similarity measures, a community social network is constructed for all participants. A community detection algorithm is further implemented to cluster travelers with similar travel behavior into the same groups. There are five clusters detected: non-working people with more shopping activities, non-working people with more recreation activities, normal commute working people, shorter working duration people, later working time people, and individuals needing to attend school. An image of activity map is built from each participant’s activity matrix. Finally, a deep learning approach with convolutional neural network is employed to classify travelers into corresponding groups according to their activity maps. The accuracy of classification reaches up to 97%. The proposed approach offers a new perspective for travel behavior analysis and traveler classification.

How do compact, accessible, and walkable communities promote gender equality in spatial behavior?

Directing growth towards denser communities with mixed-use, accessible, and walkable neighborhood design has become an important strategy for promoting sustainability, but few studies have examined whether compact development strategies could help reduce within-household gender disparities in spatial behavior by increasing accessibility. We analyze spatial behavior of heterosexual married couples in Southern California based on the 2012 California Household Travel Survey and find that households living in areas with greater regional accessibility and neighborhood walkability have smaller, more centered, and more compact activity spaces overall compared to households in less compact areas, and that married pairs living in more accessible areas have greater equality in the size and centeredness of their activity spaces. We account for residential selection bias in our multivariate analysis and find that a ten unit increase in near-residence Walk Score was associated with a 12–18% decrease in activity space size, a 6–8% decrease in residential distancing, and a 12–13% increase in spatial concentration for both men and women. Men and women, however, had significantly different activity space behaviors regardless of their neighborhood type. Compared to women, men on average had larger activity spaces and conducted their activities farther from home. Overall, results support our hypothesis that compact development provides married couples greater flexibility in how they divide household out-of-home activities by making destinations more convenient and lowering the overall spatial fixity of these activities. Future research and planning efforts should carefully consider which aspects of compact, accessible development are most effective for a given local context.

Without a ride in car country – A comparison of carless households in Germany and California

One approach to making transportation more sustainable is to transition away from a car-oriented society. Unfortunately, our understanding of the factors that prompt households to voluntarily forgo their motor vehicles is limited. The 2008 Mobility in Germany (MiD) and the 2012 California Household Travel Survey (CHTS) provide an opportunity to start filling this gap by teasing out what built environment and socio-economic variables impact the likelihood that a household is carless (voluntarily or not) in Germany and in California, two car-dependent societies with different carless rates. Results from our generalized structural equation models show that in both Germany and California, households who reside in denser neighborhoods, closer to transit stations, and who have a lower income or fewer children, are more likely to be voluntarily carless. However, households with more education are more likely to be voluntarily carless in Germany, whereas the reverse is true in California. Moreover, employment density and public transit have a higher impact on voluntary carlessness in Germany than in California. Our results also show that different socio-economic groups have substantially different residential location preferences in Germany and in California. These differences may be explained by cultural preferences, historical differences in land use and transportation policies, and by the higher cost of owning a motor vehicle in Germany.

Transportation impacts of affordable housing: Informing development review with travel behavior analysis

Planning for affordable housing is challenged by development policies that assess transportation impacts based on methodologies that often do not distinguish between the travel patterns of residents of market-rate housing and those living in affordable units. Given the public goals of providing affordable housing in areas with good accessibility and transportation options, there is a need to reduce unnecessary costs imposed by the potential overestimation of automobile travel and its associated impacts. Thus, the primary objective of this paper is to examine and quantify the influences of urban characteristics, residential housing type, and income on metrics commonly used to assess the transportation impacts of new development, namely total home-based trips and home-based vehicle trips. Using the 2010-2012 California Household Travel Survey, we regressed these metrics on urban place type, regionally adjusted income, and housing type, controlling for household size, weekday travel, and home location. The results indicate significant reductions in vehicle trip making with lower incomes and increasing urbanization. These findings support more differentiation of affordable and market-rate housing in the development review process and emphasize the need for development standards to be more sensitive to the characteristics of future residents and location.

Carless in California: Green choice or misery?

Approximately 1 million Californian households do not own a motor vehicle (hereafter a “car”). These households, who are often forgotten in transportation policy discussions, can be organized into two groups based on whether they are voluntarily carless or not. Understanding why some households decide to voluntarily forgo cars could inform policies aiming at reducing our dependency on cars. Understanding the plight of households who are not able to own a car is no less important as these households are at greater risk of social exclusion. Unfortunately, our knowledge of carless households is still sketchy so the purpose of this paper is to start filling this gap. We analyze data from the 2012 California Household Travel Survey using univariate tests and Generalized Structural Equation Models (GSEM) that account for residential self-selection to assess the impacts of various socio-economic and built environment variables on the likelihood to be carless, voluntarily or not. Our results (GSEM Model 1) indicate that carless households are more likely to have less education, a lower income, and a smaller number of members than motorized families. They also tend to live in denser, more land-use diverse, and more walkable areas with better transit coverage. Contrasting voluntarily and involuntarily carless households (GSEM Model 3), we find that involuntarily carless households are less affluent on average and they tend to live in areas that are less land-use diverse, less walkable, and with worse transit coverage. Finally, although residential self-selection is present, its impacts are minor.

Car-less or car-free? Socioeconomic and mobility differences among zero-car households

Transportation professionals have long identified the important division between choice and constraint in modal decision-making. However, while heterogeneity within some modal groups such as transit riders is well documented, intragroup differences in other groups have been largely ignored. In particular, significant heterogeneity exists among zero-car households, who may not own a car due to choice (car-free) or constraint (car-less). Recognition of intragroup heterogeneity among zero-car households yields policy implications as cities consider where to invest their ever more precious and more limited transportation resources. Using activity diary data from the 2012 California Household Travel Survey, I investigate two research questions: first, how do car-less households compare to car-free households in terms of both their socioeconomic characteristics and their relative share of zero-car households? Second, how do motivations behind not owning a car translate into mobility differences - including daily trip counts and miles traveled? I find that, contrary to media reports that the number of car-free households—that is, households that choose not to own a car—is “booming”, 79 percent of zero-car households do not own a car because of economic or physical constraints. Car-less households in many ways mirror the captive transit population, in that they have significantly lower household incomes, lower educational attainments, and are disproportionately non-white compared to car-free households. Observed socioeconomic variations translate into mobility differences, with car-free travelers taking more trips and traveling more miles per day. Policy makers should consider extending access to carshare services, which are positively associated with more trips and miles traveled among both car-free and car-less households.

Addressing the joint occurrence of self-selection and simultaneity biases in the estimation of program effects based on cross-sectional observational surveys: case study of travel behavior effects in carsharing

We estimate the effect of carsharing on travel behavior (specifically, household vehicle holdings, frequency of transit usage, and frequency of biking and walking) using data from the 2011–2012 California Household Travel Survey (CHTS). The effect of carsharing on vehicle ownership is a dynamic process that plays out over a period of time—past ownership influences enrollment decisions, which in turn influence holdings in a later period. Representing this process using cross-sectional data conflates causal effects with simultaneity bias. Further, members and non-members differ in various observed and unobserved ways—demographics, built environment of residential and workplace location, and attitudes—raising the potential for self-selection bias in comparing travel behavior between the two groups given the observational nature of the data. Drawing on established methods for dealing with each bias individually, we develop a method to help control for this joint occurrence of self-selection and simultaneity biases. Restricting the analysis to employed respondents residing in the San Francisco Bay Area, we find that 80% of the observed difference of 0.9 units in average vehicle holdings between carsharing non-members and members may be explained by the biases listed above. The remaining difference of 0.17 units reflects the estimated effect of carsharing, which is the equivalent of shedding one vehicle by about one out of every six households whose member(s) are enrolled in carsharing. The effect on transit usage and walking and biking frequency is positive, albeit small and statistically non-significant. We identify factors that may affect the internal and external validity of our results. Our methods cannot completely replace randomized experiments or panel data. However, the methods used here provide a way to help control for the joint occurrence of self-selection and simultaneity biases, and provide a ballpark estimate of causal effects, for large-scale, general-purpose, cross-sectional datasets such as the CHTS.

Automobile dependence and physical inactivity: Insights from the California Household Travel Survey

Abstract Background Auto-dependence has been linked to the physical inactivity epidemic across U.S. cities, resulting in unprecedented increases in incidences of obesity, cardiovascular diseases, depression, etc. The search for strategies to pull an overwhelming majority of auto-dependents out of their sedentary lifestyles by encouraging them to use transit, walk and bike continues to challenge planners and policy-makers. Methods We use the 2012–13 California Household Travel Survey data for analyzing the auto-dependence and physical inactivity connection. We select a sample of employed individuals with access to car in urban California, and classify them as discretionary transit riders (N=390), active auto-dependents (N=1287), or sedentary auto-dependents (N=8754) based on their self-reported travel mode use and time spent in physical activity over a 24-h period. We investigate factors that are associated with significantly high physical activity among some auto-dependents relative to the sedentary majority. We also revisit the transit-physical activity connection, and explore conditions that make transit use unfeasible for some active auto-dependents. Results Discretionary transit use is associated with higher physical activity. However, there is large variation in physical activity within auto-dependents; significantly higher physical activity is associated with factors such as higher income, flexible work schedule, shorter work hours, and mixed land use. Kids, inflexibility of work schedule, low residential density, lack of pedestrian and bicycling friendly street design, and long distance to transit stops prohibit otherwise active auto-dependents from choosing transit. Employment sector influences both physical activity and choice of transit. Conclusion To get sedentary auto-dependents out of endemic physical inactivity, our research indicates the need for targeting lower-incomes, incentivizing employers to provide flexible work hours, and to continue dense, mixed-use developments that make active travel feasible. In addition, to get active auto-dependents to use transit, transit managers must focus on retaining immigrant riders and non-Hispanic Asians, and attracting people with children.

Weather is not significantly correlated with destination-specific transport-related physical activity among adults: A large-scale temporally matched analysis.

Weather is an element of the natural environment that could have a significant effect on physical activity. Existing research, however, indicates only modest correlations between measures of weather and physical activity. This prior work has been limited by a failure to use time-matched weather and physical activity data, or has not adequately examined the different domains of physical activity (transport, leisure, occupational, etc.). Our objective was to identify the correlation between weather variables and destination-specific transport-related physical activity in adults. Data were sourced from the California Household Travel Survey, collected in 2012-3. Weather variables included: relative humidity, temperature, wind speed, and precipitation. Transport-related physical activity (walking) was sourced from participant-recorded travel diaries. Three-part hurdle models were used to analyze the data. Results indicate statistically or substantively insignificant correlations between the weather variables and transport-related physical activity for all destination types. These results provide the strongest evidence to date that transport-related physical activity may occur relatively independently of weather conditions. The knowledge that weather conditions do not seem to be a significant barrier to this domain of activity may potentially expand the universe of geographic locations that are amenable to environmental and programmatic interventions to increase transport-related walking.

Method for Gauging Usage Opportunities for Partially Automated Vehicles with Application to Public Travel Survey Data Sets

Vehicle automation has garnered a significant amount of interest in recent years. When the automated driving (AD) capability of a vehicle is assessed, it is important to distinguish between full automation, in which no human driver is required, and partial automation, in which a human driver may be required to intervene or take control of the vehicle occasionally for portions of the trip. This paper presents a method for assessing usage opportunities of partial AD in light-duty vehicle fleets. Key assumptions are that ( a) the longer the time fraction of driving in which AD is active, the better, and ( b) drivers will value having longer contiguous sections of AD-active time over having to frequently regain vehicle control. Given second-by-second records of real-world driving trips, the method uses a fuzzy inference system to estimate the fraction of driving time at a certain quality-of-use level. Performing the quality-of-use assessment for all trips and vehicles in a representative data set can then provide insight into the fraction of the population that would likely find partial AD desirable. To demonstrate the proposed method, data on vehicle trips from public travel surveys in California (California Household Travel Survey) and Atlanta, Georgia (Atlanta Regional Commission Travel Survey), were used along with simplified prototypical models for partial AD. Simulation results were generally in agreement with common perceptions but showed a wide range of possibilities, which could have been narrowed down further when more detailed trip data became available.