Household Vehicle Use Research Articles

Does the Built Environment Make a Difference? An Investigation of Household Vehicle Use in Zhongshan Metropolitan Area, China

To address worsening urban traffic and environmental issues, planners and policy makers in China have begun to recognize the importance of shaping vehicle use through the built environment. However, very few studies can be found that examine the relationship between the built environment and vehicle use in the Chinese context. With data collected in Zhongshan Metropolitan Area, this study examined how two built environment representations—simple measures and neighborhood types—were related to household car trips and motorcycle trips in China. The results of the negative binomial regression models showed that the household socio-demographic measures displayed significant association, and the built environment representations enhanced the explanatory powers. All else being equal, households in Zhongshan would generate less car and motorcycle trips if located in neighborhoods with denser land use development, better transit service and less connective street networks. In order to shape vehicle use, the findings provided informative insights for planners and policy makers to form a relatively high density of land use developments, slow down the construction of street networks, provide more jobs adjacent to residential areas and facilitate easy access to public transportation services.

Tracking National Household Vehicle Usage by Type, Age, and Area in Support of Market Assessments for Plug-In Hybrid Electric Vehicles

Plug-in electric vehicle (PHEV) technology is seen as promising technology for reducing oil use, improving local air quality, and/or possibly reducing GHG emissions to support a sustainable transportation system. This paper examines the usage of household vehicles to support assessment of the market potential of plug-in hybrid electric vehicles (PHEVs), the higher purchase price of which requires high usage rates to pay off the investment in the technology. According to the 2009 National Household Travel Survey (NHTS), about 40% of household vehicles were not used on the survey travel day [1]. This study analyzed household vehicle use and non-use by vehicle type, age, area type (metropolitan statistical area [MSA] and non-MSA), and population density. Vehicles used on survey day with or without a reported travel time and distance in the survey are considered “vehicles used”. All others are referred to as “vehicles not used”. We divided the “vehicles not used” into three categories: 1) left at home while other household vehicles were used; 2) not used because travelers used other modes; and 3) no household trips. The “vehicle used” consists of two categories: 1) those with distance and time data and 2) those with no travel data. Within these five categories, vehicles were subdivided according to four vehicle types: car, van, SUV, and pickup. Each vehicle type was further subdivided in two age groups: 10 years or less (≤10) and more than 10 years (>10). In addition, vehicle usage was compared in both MSAs and non-MSAs and during weekdays and weekends. Results indicate that most vehicles—especially pickups—are not used because the households own and use other vehicles. Moreover, SUVs—especially newer SUVs (≤10 years)—are the most utilized vehicle type and should be strongly considered as a primary vehicle type for PHEVs, in addition to cars.

Marginal-Cost Vehicle Mileage Fee

The vehicle mileage fee is a strong candidate to mitigate the funding gap in surface transportation caused by the declining purchasing power of fuel tax revenue, the emergence of hybrid and electric vehicles, and more restrictive corporate average fuel economy standards. This study computes the vehicle mileage fee on the basis of the marginal cost of travel and internalizes various externalities such as congestion, infrastructure deterioration, pollution emissions, and greenhouse gas emissions. Multiple regression models and discrete choice models are developed on the basis of the 2009 National Household Travel Survey data to analyze the impacts of the proposed marginal-cost vehicle mileage fee on vehicle ownership, fuel efficiency, vehicle miles traveled, energy consumption, emissions, and equity. In addition, the sensitivity of these impacts to exogenous fuel price volatility is estimated quantitatively. Results show that with consideration of all aforementioned externalities, the marginal-cost vehicle mileage fee by vehicle make and model would range from 7.7 to 9.1 cents/mi, which is much higher than the per mile equivalent of the current fuel taxes (about 1.2 cents/mi). Household vehicle use behavior is much more sensitive to the marginal-cost vehicle mileage fee than vehicle ownership decisions, with a significant (27.1%) reduction in vehicle miles traveled, but a minor increase in vehicle fuel efficiency (up to 4.2%). Nevertheless, the marginal-cost vehicle mileage fee can reduce energy consumption and pollution or greenhouse gas emissions by about a fourth. These sustainability benefits are even more significant if fuel prices continue to increase. Without consideration of the benefits from revenue redistribution, lower-income households, as expected, would be hurt more than higher-income households (1.3%).

Six-Wave Odometer Panel for Evaluation of Voluntary Travel Behavior Change Programs

A six-wave panel survey of household vehicle use in Adelaide, South Australia, and Melbourne, Victoria, was reviewed, with odometer readings as the key measurement. The study was conducted to explore the feasibility of using this method to evaluate the effectiveness of voluntary travel behavior change (VTBC) projects implemented at the household level. Odometer surveys are a practically feasible, publicly acceptable, and reliable method for measuring change in household vehicle use. The collection of odometer readings was chosen because it was a direct way to quantify distance traveled in private vehicles that was difficult to manipulate or misreport. The panel design was useful to minimize variation, and burden was reduced by keeping the task simple. Although the sample obtained may have been subject to coverage error, the demography of the sample is stable over time; this status indicates that there is no selective dropout from particular population segments. The data collected in this pilot suggest that sample sizes upwards of 1,000 households would be necessary to conclude with 95% confidence that an actual difference in daily vehicle kilometers traveled (VKT) per vehicle of ±1 km was significantly different from zero. Although the data here show that the average daily VKT per household and per vehicle in Adelaide stayed stable for VTBC nonparticipants and dropped in participating VTBC households, the small sample size means it cannot be stated with statistical confidence whether this was significant.

An Almost Ideal Demand System Model of Household Vehicle Fuel Expenditure Allocation in the United States

In this study I model vehicle-fuel expenditure allocation in multi-vehicle households based on the Almost Ideal Demand System (AIDS). Using data from surveys conducted by the Energy Information Administration in 1988, 1991 and 1994, I estimate the AIDS model, augmented with a comprehensive set of household and vehicle characteristics for households owning 1 to 4 vehicles ordered by vehicle age. Results show that vehicle characteristics are the most significant factors in the expenditure allocation process. Mean and standard deviation of price, expenditure and Allen substitution elasticities are calculated across households. Own-price elasticities for all vehicles are close to 1. Allen substitution elasticities indicate that all vehicle pairs are substitutes, and only vehicle 1 is found to be expenditure inelastic. The approach taken in this study enables a disentangling of vehicle allocation/substitution effects from aggregate household vehicle use behavior. This will be useful in the analysis of efficiency and distributional effects of policies affecting household transportation.

Using Nationwide Personal Transportation Survey Data to Indicate Electric Vehicle Market Potential in Rural Areas

The 1990 Nationwide Personal Transportation Survey included 10,381 rural households, that is, households that were not in metropolitan statistical areas and were in zip code areas with population densities of less than 386 persons/km2 (1,000 persons/mi2). An analysis of the survey data to determine how rural households use their household (nonfarm) vehicles and to describe the potential for some vehicles to be electric vehicles (EVs) is reported. A main step in the analysis is to aggregate the daily household trips by vehicle. The counterintuitive result is that rural household vehicle use in terms of kilometers per day is quite similar to that of metropolitan area households. Thus, the potential for rural households to acquire EVs is similar to that for metropolitan area households.

Using Nationwide Personal Transportation Survey Data To Indicate Electric Vehicle Market Potential in Rural Areas

The 1990 Nationwide Personal Transportation Survey included 10,381 rural households, that is, households that were not in metropolitan statistical areas and were in zip code areas with population densities of less than 386 persons/km2 (1,000 persons/mi2). An analysis of the survey data to determine how rural households use their household (nonfarm) vehicles and to describe the potential for some vehicles to be electric vehicles (EVs) is reported. A main step in the analysis is to aggregate the daily household trips by vehicle. The counterintuitive result is that rural household vehicle use in terms of kilometers per day is quite similar to that of metropolitan area households. Thus, the potential for rural households to acquire EVs is similar to that for metropolitan area households.

Demand for electric vehicles in hybrid households: an exploratory analysis

Previous studies of the potential market for battery electric vehicles (BEVs) from different research streams have failed to converge on a single, robust estimate. What these previous research streams share are untested or implausible assumptions about consumer response to new transportation technology. We frame the BEV purchase decision in terms of a household's entire stock of vehicles, car purchase behavior and travel behavior. Within this framework, households which own both electric vehicles and gasoline vehicles are called ‘hybrid households’. Because nearly all consumers are unfamiliar with the characteristics of BEVs, we designed an interactive interview based on week-long travel diaries, which we call Purchase Intentions and Range Estimation Games (PIREG) to explore hypothetical hybrid household vehicle use. Our primary finding is that consumers' perceived driving range needs are substantially lower than previous hypothetical stated preference studies conclude. We find evidence of a viable market for BEVs with 60 to 100 miles driving range.

An econometric model of vehicle use in the household sector

Vehicle-use modelling at the household level has taken on new importance with the pressures on governments to encourage more efficient utilisation of increasingly scarce nonreplenishible liquid fuels. The fundamental energy equation recognizes two direct influences on consumption—the fuel efficiency of the vehicle and the amount of use. Until recently, the interrelationship between vehicle choice and vehicle utilisation at the household level was acknowledged but ignored. The availability of reliable vehicle-use data at the household level now enables a more serious effort at amending the imbalance of research effort where the reliance has been predominantly on vehicle choice modelling and gross (exogenous) assumptions on utilisation as a basis for predicting fuel consumption. This paper proposes an econometric method for identifying the influences on household vehicle use. It differs from previous empirical work in that vehicle kilometers, fuel cost per kilometer and vehicle fuel efficiency are endogenous, with utilisation of each vehicle endogeneously dependent on the utilisation of each and every household vehicle. The data are drawn from wave 1 of a four-wave panel of 1436 households in the Sydney metropolitan area. The empirical findings expose a set of influences on use hitherto not considered. The model specification provides an appropriate module for integration with household-based discrete choice models of vehicle choice.

Estimating daily vehicle usage distributions and the implications for limited-range vehicles

Understanding the potential market for limited-range vehicles is important to planning research and development programs for electric and hybrid vehicles and for gaseous-fueled vehicles as well. Studies of consumer preferences and perceptions have shown vehicle range to be a very important vehicle attribute. Studies of household vehicle use, on the other hand, have suggested that the range requirements most households place on vehicles are quite modest. The latter, however, have been severely limited by the absence of longitudinal data on the usage of individual vehicles. Instead, they have relied on single-day surveys on many vehicles, an inappropriate data source. This study develops a method for estimating daily travel distributions for individual vehicles and applies it to a recent longitudinal survey of miles and days between refuelings for over 2000 vehicles. Every vehicle in the sample has at least 30 consecutive refueling intervals. A variety of measures of “range requirement” are defined and calculated. The results confirm the existence of a substantial potential market (20–50% of all household vehicles) for vehicles with ranges on the order of 100 miles. Future research using these data and this method could describe the nature of vehicles with limited-range needs and the households which own them.