California Household Travel Survey Research Articles

Collaborative Vehicle-to-Grid Operations in Frequency Regulation Markets

Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform’s pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform’s expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133 .

The Mode is not the Methods: Assessing Changes in Biking, Walking and Transit in California using the 2012 CHTS and 2017 NHTS

Data from the 2012 California Household Travel Survey (CHTS) and the 2017 National Household Travel Survey (NHTS) suggest that biking, walking, and transit use in California decreased over this five-year period. In light of the California Department of Transportation (Caltrans) and State Transportation Agency (CalSTA)’s goal of tripling walking and doubling biking and transit use in their most recent strategic plan, these unanticipated results raised concerns about whether these decreases stem from methodological differences and choices about analysis between the two surveys. In this study, we evaluate numerous differences between the two surveys to assess whether the changes are likely to be real, or the result of methodological differences between the CHTS and the NHTS. We find that overall, the use of biking, walking and transit decreased over this time period, and these results are generally consistent across methodological differences and analysis choices.

Who’s in the driver’s seat? Gender and the division of car use in auto-deficit households

• Practical necessity—the amount of time spouses that spend on household-serving or work-related activities—is the primary determinant of automobile use for both men and women. • Women are more likely to be the primary houseworker than men. • Women have substantially more exclusive access to the household vehicle than their male partners. • The findings reflect the persistence of the gender division of household labor and the role of the car in its maintenance. • The use of alternative modes of transportation is high among spouses who travel when the vehicle is in use by their partners. We examine the role of gender in access to household resources, in particular the household automobile. Drawing on data from the 2012 California Household Travel Survey, we isolate the determinants of vehicle use within dual-earner, dual-driver, heterosexual households in which drivers share a single vehicle. We test four gender-related hypotheses: the role of disparities in economic power, practical necessity related to the household division of labor, gender norms, and gender preferences. We find that practical necessity—the amount of time that spouses spend on household-serving or work-related activities—is the primary determinant of automobile use for both men and women. In contrast to previous research, we find that women have substantially more exclusive access to the household vehicle than their male partners. Rather than a measure of equality, this finding reflects the persistence of the gender division of household labor and the role of the car in its maintenance. The study underscores the broader need for policies to equalize gender roles both within and between the home and the workplace, as well as a role for transportation policies that serve the needs of household members who do not have primary use of the household car.

Impact of carsharing on the mobility of lower-income populations in California

Mobility is an important quality of life element, with automobile ownership regularly playing an essential role in the mobility of lower-income populations. While carsharing has gained significant momentum in the past decade as an alternative to private vehicle ownership, less is known about the role of carsharing as a mobility mode for lower-income households. To address this gap, this paper examines the impact of carsharing on the mobility of lower-income populations. This study analyzes data from the 2012 California Household Travel Survey by employing a Structural Equation Modeling (SEM), which controls for residential self-selection, car-ownership and carsharing endogeneity issues, and simultaneity bias. Model results indicate that while carsharing membership enhances mobility for both lower- and higher-income households, it has a more substantial effect on mobility among lower-income families, primarily when it is coupled with public transportation services. The findings of this study underscore the importance of the coordination between carsharing operators and public transportation agencies for enhancing the mobility of lower-income populations.

Neighborhood Effects of Safe Routes to School Programs on the Likelihood of Active Travel to School

Safe routes to school (SRTS) programs aim to increase the share of students commuting to school by active modes (e.g., walking and cycling). This study measures the relationship between the presence of SRTS programs in a neighborhood and children’s journey-to-school mode choice. Children were identified from households in the 2012 California Household Travel Survey and they were classified based on whether they commuted to school by active modes. Next, census tracts with SRTS programs were identified based on the presence of data in the National Center for Safe Routes to School (NCSRTS) data collection system. Based on these two datasets, a logistic regression model estimated the likelihood that a child commuted to school by active modes, based on the presence of a SRTS program and controlling for individual, household, and tract characteristics. This analysis was supplemented with stakeholder interviews about the nature of SRTS programs within the study area and how they are perceived. Findings indicate that longer trip distance and race (relative to white students) are associated with reduced rates of active travel to school, but that these differences are mitigated by the presence of SRTS programs. Interviews suggest SRTS programs in the study area primarily emphasize education and encouragement rather than engineering interventions. It was concluded that the effect of such SRTS programming might best be described as reducing barriers to active school travel rather than simply increasing the likelihood of using active modes.

Collaborative Vehicle-to-grid Operations in Frequency Regulation Markets

We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, these individually-owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment enabling V2G. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, taking into account their heterogeneous driving schedules. As EV adoption progresses past the nascent stage, V2G business models become more viable. V2G participation in frequency regulation provides a valuable resource to stabilize the electric grid against fluctuations between demand and supply, and complements the continued growth of clean but intermittent renewable (e.g., solar and wind) power. On the other hand, successful implementation of V2G provides another economic incentive for switching to EVs and potentially helps sustain adoption growth, especially as government subsidies phase out. Such operations are difficult to plan for, as it is important to model both the transportation (i.e., travel schedule) and energy (operations of regulation market) features of EV fleets. We model the platform's pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EVs to plug in their EVs, and capacity bids in the regulation market; At the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally-robust optimization techniques to formulate the problem as a mixed-integer second-order cone program, which can be efficiently solved with commercial solvers. We further derive a class of valid inequalities for the problem based on the theory of complements and substitutes of convex cost network flow problems. We conduct a computational study based on the California Household Travel Survey dataset and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform's expected profits. We also find stark differences between the optimal rebate schedules under a profit-maximizing platform and the alternative model where the platform is collectively owned by the drivers. Our findings shed lights on business model design of V2G systems.

Developing policy thresholds for objectively measured environmental features to support active travel

A novel evidence-based methodology is presented for determining place-based thresholds of objectively measured built environment features’ relationships with active travel. Using an innovative machine-learning based Generalized Additive Modeling framework, systematic heterogeneity fundamental to the development of well-justified and objective environmental thresholds is accounted for. The methodology is employed to model an individual’s likelihood of transport walking as a function of environmental factors using California Household Travel Survey linked with comprehensive built environment data. The results reveal strong and complex non-linear dependencies of likelihood of transport walking on environmental features that cannot be quantified using standard threshold detection methods. Thresholds for key environmental features to enhance active travel vary significantly across different socioeconomic groups. Accounting for strong income-based differences in development of environmental benchmarks is emphasized. The thresholds can serve as a useful guiding tool for policymakers, planners, engineers, and public health officials to track existing environmental conditions and healthy behaviors.

Solving the winner determination problem in combinatorial auctions for fractional ownership of autonomous vehicles

AbstractThe introduction of autonomous vehicles (AVs) to consumer markets will expedite the trend of car sharing and enable co‐owning or co‐leasing a car. In this paper, we consider a combinatorial auction market for fractional ownership of AVs, which is unique in two aspects. First, items are neither predefined nor discrete; rather, items are continuous time slots defined by bidders. Second, the spatial information of bidders should be incorporated within the winner determination problem (WDP) so that sharing a vehicle is indeed a viable plan. The consideration of spatial information increases the computational complexity significantly. We formulate the WDP, which plays a critical role in various auction designs and pricing schemes, for both discrete‐ and continuous‐time settings. In terms of social welfare maximization, we show that the continuous‐time model is superior to the discrete‐time model. We provide a conflict‐based reformulation of the continuous‐time model, for which we develop an effective solution approach based on a heuristic and maximal clique based reformulations. Using samples of the 2010–2012 California Household Travel Survey, we verify that the proposed solution methods provide effective computational tools for the combinatorial auction with bidder‐defined items.

Parking behaviour: The curious lack of cruising for parking in San Francisco

Cruising for parking has long been perceived as a major source of congestion and emissions in urban areas, but recent empirical work suggests that parking may not be as onerous as folklore suggests, and that the amount of vehicle travel attributable to cruising is minimal. In this paper, we reconcile these perspectives through a dynamic programming model of parking search, and empirical insights from a large-scale GPS dataset in San Francisco and the California Household Travel Survey. We first draw a conceptual distinction between parking search, the time between the driver’s decision to park and when a parking space is taken; and cruising, defined as excess vehicle travel due to parking search. In places with little or no through traffic, up to half of traffic can be searching for parking, but cruising can be zero. We then operationalize this distinction through a dynamic programming model. The model predicts that when parking is perceived to be scarce, drivers are more willing to take a convenient available space, even if it is some distance from their destination. Counter-intuitively, scarce parking can even suppress vehicle travel as perceived parking scarcity leads drivers to stop short of their destinations and accept a longer walk. Empirical data from California indicate that neighborhood density (a proxy for parking availability) has little impact on cruising for parking, but increases walk distances from parking locations to final destinations. We conclude that cruising for parking is self-regulating, and that in certain circumstances parking scarcity can even reduce vehicle travel.

Parking prices and the decision to drive to work: Evidence from California

This paper explores the impact of parking prices on the decision to drive to work using a California household travel survey dataset and a discrete choice model. The paper tackles estimation challenges posed by insufficient parking information. The first challenge is the estimation of parking prices for those who do not drive, which is addressed by using a sample selection model. The second challenge is to understand the effect of the extent of the prevalence of Employer-Paid parking coupled with incentive programs offered in-lieu of parking. To address this challenge, two extreme scenarios are examined, and a range for the marginal effects of parking prices is estimated; one scenario assumes everyone receives Employer-Paid parking coupled with in-lieu of parking incentives, and the second assumes that no one is offered such incentives. The results suggest that higher parking prices reduce driving, regardless of the followed approach. It is estimated that a 10% increase in parking prices leads to a 1–2 percentage point decline in the probability of driving to work. This range varies with initial parking prices, where the lower end of the range increases at a decreasing rate, and the higher end peaks at $2.5 and decreases with higher prices. Moreover, there seems to be no evidence of sample selection bias. The evidence confirms that parking pricing can indeed be an effective transportation demand management tool.

Does Transit-Oriented Gentrification Increase Driving?

Do transit-oriented development (TOD) programs cause people to drive more by displacing poorer households from transit-rich neighborhoods? We analyzed the California Household Travel Survey and the Nationwide Household Travel Survey along with selected Census data and found that higher income households reduce vehicle miles traveled (VMT) more when living in TODs than do poorer households. Results from controlled analysis were similar. In addition, gentrifying neighborhoods near rail stations in California have usually become more densely populated in the last two decades. Thus, in many cases, the likely net effect of gentrification has been regional reductions in VMT. But gentrification-induced displacement without densification will likely increase VMT.

Toward a Richer Picture of the Mobility Needs of Older Americans

Problem, research strategy, and findings: People older than 65 are the most rapidly growing segment of the U.S. population, yet our cities and transportation systems are not age friendly. Low-income, minority, older adults residing in inner-city neighborhoods are largely transit dependent, rely significantly on walking for transportation, and thus have particular mobility needs. We used a mixed-methods approach that drew information from the California Household Travel Survey but also from direct interaction (through focus groups, interviews, and neighborhood walking audits) with 81 low-income, inner city–living older adults to understand their travel patterns and mobility challenges and needs. We find that despite some positive mobility indicators in the inner city (mixed-use environment, frequent bus service, and short travel distances), these elders face significant mobility challenges because of a deteriorated built environment, heavy traffic, homelessness, and crime. A limitation of this research is that the small sample did not allow the study of possible gender or race/ethnicity differentiation in the travel patterns and needs of older adults.Takeaway for practice: Planners should not rely only on information from the census and other aggregate data sources to understand the mobility needs of older adults but should complement this information with direct interaction with the communities for which they are planning. Although some social problems limiting the mobility of older adults are difficult to tackle, environmental and streetscape improvements can significantly enhance their mobility.

Why do they live so far from work? Determinants of long-distance commuting in California

The determinants of long-distance commuting (trips longer than 50 miles one-way) in the U.S. appear to be poorly understood even though long-distance commuting likely has substantial environmental, social, and economic impacts. A review of the literature shows that that few papers have considered how housing costs influence long-distance commuting. Moreover, residential self-selection has rarely been accounted for in commuting studies. To start addressing these gaps, we analyze the long-distance travel component of the 2012 California Household Travel Survey via a generalized structural equation model. In our model, land use and housing costs are explained by household and head of household characteristics; together with these characteristics, land use and housing costs influence long-distance commuting. We find that the probability of a household commuting long-distance decreases with an increase in the job-housing ratio (OR = 0.914***) and most importantly, with the median home value of the census tract where a household resides (OR = 0.488***). Conversely, this probability increases with the median census tract home values where household members work (OR = 1.765***). Finally, our model results confirm the presence of small residential self-selection effects. These results highlight the importance of providing more affordable housing and mixed development options to reduce long-distance commuting and its associated environmental impacts.

Lifestyles, residential location, and transport mode use: A hierarchical latent class choice model

This study develops a hierarchical latent class choice model that captures the concurrent influence of lifestyles on household residential neighbourhood location and individual transport mode use decisions. The model is empirically evaluated using data from the 2010–12 California Household Travel Survey. The model identifies six household-level classes that differ in terms of their preferences for different neighbourhood attributes when deciding where to live and their household characteristics. Coincidentally, the model also identifies six individual-level classes that differ in terms of the travel modes that they consider when deciding how to travel, their sensitivity to different level-of-service attributes, and their individual characteristics. Household preferences for neighbourhood types and individual preferences for travel modes show expected patterns of correlation. In general, households that prefer to live in suburban neighbourhoods are more likely to consist of individuals that are car-dependent, and households that prefer to live in inner-city neighbourhoods are more likely to consist of individuals that are multimodal. However, our analysis also reveals interesting patterns of deviation. For example, high-income migrant households and median-income white households display strong preferences for suburban neighbourhoods, but individuals belonging to these households also have a high likelihood of being multimodal, with a strong preference for bicycling. We discuss how these patterns of correlation can be used to inform transport and land use policy in novel ways.

Physical Accessibility and Employment among Older Adults in California

Older adults are delaying retirement and remaining in the paid workforce longer than in previous decades. There are many potential explanations for this trend. In this study, it is hypothesized that the ease or difficulty of traveling may significantly influence the labor force participation of older adults, just as it does for other working-age adults. As they age, older adults can face a number of barriers to mobility. The hypothesis is tested using data from the 2012 California Household Travel Survey (CHTS) and propensity score matching. The paper focuses on the effects of automobile ownership and transit access on the employment status of older adults (60+), controlling for a host of characteristics associated with the likelihood of employment. The analysis shows that transportation access has a substantial and positive association with employment for older adults, particularly older adults living in low-income households (those earning less than $35,000 per year). Access to jobs by public transit is especially influential among low-income older adults who live in households without automobiles. The findings underscore the importance of enhancing the transportation environment such that it allows older adults to travel regularly on their own by car or, in dense urban neighborhoods, by public transit. Limitations to this study suggest the need for additional quantitative analysis of longitudinal data as well as qualitative analysis of data from interviews and focus groups.

Optimizing the electric range of plug-in vehicles via fuel economy simulations of real-world driving in California

This paper presents a modeling and simulation study of the greenhouse gas (GHG) emissions of plug-in vehicles subject to real-world driving conditions. The simulations utilize open-source software tools for estimation of second-by-second energy and/or fuel consumption in more than 65 thousand real-world recorded trips in the California household travel survey (CHTS) data set. Statistical distributions of the equivalent well-to-wheels (W2W) GHG emissions corresponding to CHTS trips are generated for three baseline public-domain plug-in vehicle models of: (i) parallel-drive plug-in hybrid, (ii) serial-drive plug-in hybrid, and (iii) pure electric vehicle. In addition to the baseline models, several derived vehicle models are created by upsizing of the battery and powertrain via an iterative procedure to attain the new all-electric range (AER). The emissions model also considers days when a pure electric vehicle cannot meet the travel demand by assuming that the driving on those days is done on a conventional vehicle. For each of the considered powertrain architectures, optimizing the AER for minimum GHG emissions becomes a trade-off between reducing the gasoline driven miles and maintaining reasonably sized (lightweight) powertrain components. As such, the equivalent GHG emissions of a vehicle are shown to have diminishing returns in terms of AER, and the optimum AER depends on both the carbon content in the electric grid, as well as the charging behavior. Within considered modeling assumptions and several vehicle usage conditions, the optimum AER is estimated to be between 30 and 60 miles for PHEVs, and between 120 and 200 miles for BEVs.

Modeling electric vehicle adoption considering a latent travel pattern construct and charging infrastructure

This paper presents a behavioral model of public, revealed preferences (RP) for various types of electric vehicles (EVs) while accounting for a latent (green) travel pattern construct and charging infrastructure characteristics. Specifically, a two-level nested logit (NL) model is estimated to explain households’ fuel type choice among five alternatives and three nests: (1) battery electric vehicles (BEVs); (2) hybrid vehicles including hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs); and (3) conventional vehicles including gasoline and diesel vehicles. Further, a latent travel pattern construct which captures a week-long number of trips by non-vehicle travel modes as well as daily vehicle and tollway use is estimated in a structural equation setting and subsequently fed into the NL model. Using a recent RP dataset from the California Household Travel Survey, we identify market segments for alternative fuel types based on households’ socio-economic characteristics, built environment factors concerning public plug-in EV (PEV) charging infrastructure characteristics, latent and observable travel behavior factors of a household vehicle’s principal driver, and vehicle attributes. The results highlight that the number of public PEV charging stations is only significant for households choosing PHEVs and interestingly insignificant in the BEV utility. Furthermore, the sensitivity analysis of the findings reveals that PHEV users are elastic with respect to household vehicle ownership ratio and the latent green travel pattern construct, while BEV users are inelastic to any explanatory variable.

Fragmentation in Daily Schedule of Activities using Activity Sequences

A new method of sequence analysis to measure fragmentation in activity participation is presented in this paper. We applied this method to a sample of residents in the Central Coast of California that participated in the California Household Travel Survey in 2012–2013. This method explores sequences of daily activity and travel employing techniques from the sequencing of events in the life course of individuals. Studying sequences of daily episodes (each activity and each trip) is preferable to other techniques of studying activity-travel behavior because sequences include the entire trajectory of a person’s activity during a day while at the same time considering the number of activities, order of activities in a day, and their durations jointly. We found substantial fragmentation in activity participation among persons with children and in specific age groups (25–65) amplified by the presence of children in the household. We also found poverty plays an important inhibiting role. Examinations of the days of the week showed significant and substantial differences among the days with both Sundays and Saturdays being distinct, but also substantial differences among the weekdays. The paper provides details about this new technique and the statistical analysis of fragmentation. It also provides a discussion about future steps.

Modeling the Effect of Power Consumption in Automated Driving Systems on Vehicle Energy Efficiency for Real-World Driving in California

Vehicle automation has drawn much attention in recent years, as it is perceived to usher in new levels of safety, convenience, and energy efficiency in transportation. Much uncertainty and speculation still exist regarding how automated driving (AD) would affect the overall transportation energy, as a result of some factors that are difficult to predict, such as changes in driving patterns and induced travel demand. There is also much speculation about the optimum vehicle powertrain for which AD systems are to be mounted. This study focuses on a less discussed, less speculative issue that pertains to both transportation energy efficiency and powertrain suitability. The impact of the power consumption in an AD system (for sensors, data processing, and vehicle controls) is analyzed for various powertrains via a publicly open-source simulation code, for more than 59,000 real-world vehicle trips obtained from the California Household Travel Survey. Study of scenarios of power consumption in the AD system that range from present-day values (about 3 kW) to future targets (0.5 kW) reveal interesting trends in vehicle energy efficiency. At 0.5 kW power consumption, the AD system can be of minimal impact to vehicle efficiency; however, at present-day levels of AD power consumption, the electric driving range (for electric vehicles and plug-in hybrids) could be shortened by 27–47% and fuel consumption could increase by up to 37% compared with the same vehicle model with no AD system.

How do they get by without cars? An analysis of travel characteristics of carless households in California

In spite of their substantial number in the U.S., our understanding of the travel behavior of households who do not own motor vehicles (labeled “carless” herein) is sketchy. The goal of this paper is to start filling this gap for California. We perform parametric and non-parametric tests to analyze trip data from the 2012 California Household Travel Survey (CHTS) after classifying carless households as voluntarily carless, involuntarily carless, or unclassifiable based on a CHTS question that inquires why a carless household does not own any motor vehicle. We find substantial differences between our different categories of carless households. Compared to their voluntarily carless peers, involuntarily carless households travel less frequently, their trips are longer and they take more time, partly because their environment is not as well adapted to their needs. They also walk/bike less, depend more on transit, and when they travel by motor vehicle, occupancy is typically higher. Their median travel time is longer, but remarkably, it is similar for voluntarily carless and motorized households. Overall, involuntarily carless households are less mobile, which may contribute to a more isolated lifestyle with a lower degree of well-being. Compared to motorized households, carless households rely a lot less on motor vehicles and much more on transit, walking, and biking. They also take less than half as many trips and their median trip distance is less than half as short. This study is a first step toward better understanding the transportation patterns of carless households.