Vehicle Ownership Decisions Research Articles

Decoding Private or Commercial Vehicle Ownership Decisions for Low-Carbon Mobility Transitions: A Systematic Review of the Literature

Increase in vehicle ownership (VO) as a result of rapid economic and population growth and its negative environmental effects have gradually become a potential threat to the physical and social environment. A wide range of factors influences the propensity for VO by individuals or firms; however, the existing body of literature lacks a critical review which provides an understanding of their underlying associations and potential implications for policymaking in one holistic frame. To fill this research gap, this study presents a systematic review of factors affecting private and commercial VO using the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). The factors influencing private VO decisions are extracted from the reviewed articles; subsequently, the identified factors are topologically divided into seven categories. The findings show that most studies identified the factors contributing to VO as related to socio-economic, demographic, and built environmental characteristics. A lack of research has been identified for attitudinal, technological, and parking-related variables. It is also concluded that there are gaps in the knowledge base on factors contributing to the commercial VO of a business establishment. Designing informed policy measures based on such factors that can induce a sustainable transition in the mobility sector is important to mitigate transportation-related negative externalities on the environment. The overall review findings offer actionable guidance for low-carbon mobility transition and outline a future research agenda for academic researchers and decision makers. The quantifiable factors revealed in this study are expected to assist policy makers in discouraging VO propensity among individuals or firms.

Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions

In recent years, planners have started considering Machine Learning (ML) techniques as an alternative to discrete choice models (CM). ML techniques are primarily data-driven and typically achieve better prediction accuracy compared to CM. However, it is hypothesized that since the ML techniques do not have the strong grounding to economic theory as the CMs, they may not perform well in contexts that are radically different from the ‘training’ scenario. It is also hypothesized that the relative prediction performance may be affected by the metrics used for comparing the models. This research aims to test these two hypotheses empirically by modelling vehicle ownership choices using household survey data from Dhaka, Bangladesh collected in 2004, 2010 and 2019. The performances of CM (multinomial logit) and ML techniques (neural networks and gradient boosting trees) have been compared using log-likelihood and mean absolute percentage error of market shares. The results indicate that the multinomial logit model (MNL) with a piecewise linear transformation of the household income, has the best performance in terms of log-likelihood and mean absolute percentage error of market shares. This is followed by Neural Networks (NN) and Gradient Boosting Trees (GBT). The results thus provide empirical evidence that the ML techniques do not consistently outperform CM. Moreover, the difference in the performance of the models further increases if the prediction scenario is substantially different. This reinforces the hypothesis that CMs, with their behavioural underpinning, are better suited for long-term forecasting than data-driven ML approaches, especially if the population and network attributes are expected to change substantially. These findings will be useful for planners and policy makers in the selection of the appropriate tool for forecasting travel demand.

Effects of trip-level characteristics on autonomous vehicle ownership: A U.S. analysis

We examined how trip-level characteristics may influence autonomous vehicle ownership decisions. Using 639 stated preference survey responses in the US, we developed an integrated choice and latent variable model linking trip- and consumer-level characteristics to AV ownership choices. Results suggested parking cost, travel cost and travel time are important predictors for vehicle ownership at the trip-level; and monthly payment, car ownership importance and autonomous vehicle safety perceptions are significant predictors at the consumer-level. By examining elasticities of vehicle ownership market shares, we found while consumer-level characteristics have the most prominent effect on vehicle ownership decisions, trip-level characteristics have a small but measurable effect. Among trip-level characteristics, parking cost has the largest impact on vehicle ownership decisions, followed by travel cost of ridehailing (both conventional and autonomous), and transit travel time. Finally, we simulated future vehicle ownership decisions under three policy scenarios by leveraging trip- and consumer-level characteristics.

The role of residential choice on the travel behavior of young adults

Several studies have shown that the travel behavior of young adults in the United States in the past two decades differed from that of prior generations. On average, recent young adults drove fewer miles, owned fewer vehicles and made use of public transit more often. A higher share of young adults also chose to live in cities. This study examines the relationship between the location decision of young adults and their travel behavior. We examine how being a young adult and other socioeconomic variables are associated with residential location decisions, and how these in turn affect vehicle ownership, mode choice and travel distance. Our analysis uses household travel survey data from the Seattle regions collected in 2006 and 2017 and employs a recursive structural equation model to examine these questions. We find that young adult households were more likely to live closer to the city center and to have fewer vehicles than older ones. Fewer young adults also chose to own vehicles in 2017 than in 2006. While young adults made more use of non-automobile modes and had fewer person miles traveled, we find that these effects were more due to their residential location and vehicle ownership decisions than due to direct preferences about mode or distance traveled. These findings suggest that significant changes would be expected in the mode use and miles traveled among young adults if their residential location or vehicle ownership preferences change significantly due to life cycle or other factors.

Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm

In the United States, several studies have looked at the association between automobile ownership and sociodemographic factors and built environment qualities, but few have looked at household travel characteristics. Their interactions and nonlinear linkages are frequently overlooked in existing studies. Utilizing the 2017 US National Household Travel Survey, the authors employed an extreme gradient boosting tree model to evaluate the nonlinear and interaction impacts of household travel characteristics and built environment factors on vehicle ownership in three states of the United States (California, Missouri, and Kansas) that are different in population size. To develop these models, three main XGBT parameters, including the number of trees, maximal depth, and minimum rows, were optimized using a grid search technique. In California, the predictability of vehicle ownership was driven by household travel characteristics (cumulative importance: 0.62). Predictions for vehicle ownership in Missouri and Kansas were dominantly influenced by sociodemographic factors (cumulative importance: 0.53 and 0.55, respectively). In all states, the authors found that the number of drivers in a household plays a vital role in the vehicle ownership decisions of households. Regarding the built environment attributes, deficiencies in cycling infrastructure were the most prominent attribute in predicting household vehicle ownership in California. This variable, however, has threshold connections with vehicle ownership, but the magnitude of these relationships is small. The outcomes imply that improving the condition of cycling infrastructure will help reduce the number of vehicles. In addition, incentives that encourage the households’ drivers not to buy new vehicles are helpful. The outcomes of this study might aid policymakers in developing policies that encourage sustainable vehicle ownership in the United States.

Examining the impact of car-sharing on private vehicle ownership

Abstract Car-sharing has experienced a significant boom in recent years, with estimates suggesting that car-sharing programs are now operating in over 30 countries worldwide, serving around five million users. The potential to reduce individual vehicle ownership rates is frequently cited as a motive for promoting car-sharing. While some previous studies have argued that customers are indeed willing to reduce the total number of vehicles owned after becoming car-sharing members, the reliability of these findings is tenuous given that many are based on self-selected samples of car-sharing users, resulting in biased estimates. In theory, the availability of car-sharing programs could have limited effect on the general public’s car ownership decisions–or at least have no effect on a large portion of travelers. Whether or not traveler decision processes are significantly influenced by specific attributes of different car-sharing options (e.g., access time, vehicle size, fuel type, etc.) remains an unanswered question, as there are limited quantitative studies on this issue. To contribute to filling this research gap, this paper discusses the findings of a study of 1,500 private households across major Australian cities. A nested logit model is used to investigate the impacts of car-sharing on respondents’ household vehicle ownership decisions. In contrast to the results of some previous studies, we find that the stated availability of car-sharing appears to have minimal impact on respondents’ decision to own a vehicle or not, leading to important policy implications. We agree with prior investigations that car-sharing could potentially reduce private car ownership. However, because this study finds limited impact of the availability of car-sharing on vehicle ownership, and because the majority of respondents did not self-identify as car-sharing users, education and awareness campaigns could be important factors in improving the general public’s preferences towards car-sharing and fully realizing car-sharing’s benefits to society.

An exploratory analysis of traffic accidents and vehicle ownership decisions using a random parameters logit model with heterogeneity in means

The interaction between traffic accidents and household vehicle ownership decisions has not been thoroughly explored in literature. This interaction is worthy of investigation as the outcome of traffic accidents involving property damage significantly affects the household members’ overall mobility levels and car fleet utilization and renewal process. In this study, we present a methodology that addresses a driver’s decisions pertaining to a damaged car following a traffic accident. The choice set includes three alternatives: to replace the damaged car with a new one, replace the damaged car with a used one, and repair the damaged car. A random parameters (mixed) logit model with heterogeneity in the means is specified and estimated to gain more insight into the driver’s decision-making process following a traffic accident.The estimation results of the random parameters (mixed) logit model with heterogeneity in the means indicate that a wide range of explanatory variables affect the driver’s decision to replace a damaged car with a new or used one. For example, as the repair cost to the car value ratio increases, the likelihood of the driver replacing the damaged car with a new one increases. The marginal effect of not repairing a damaged car is twice that of replacing a damaged car with a new car. Additionally, traffic accidents causing critical injuries to drivers decrease the likelihood of replacing a damaged car with a used car relative to accidents that do not generate critical injuries. Other explanatory variables that are likely to increase the propensity of drivers to replace damaged cars with new ones include the car age, type of insurance policy, total household annual income, driver age, and number of workers at the household. Clearly, a driver’s decision to repair or replace a damaged car should not be viewed as a discrete or isolated decision. The stochastic component of traffic accidents (driver decisions pertaining to damaged cars) should be accounted for when formulating changes in household vehicle ownership levels and utilization decisions.

Modeling household housing and car ownership decisions: a group decision-based time allocation approach

ABSTRACT Multitudes of household long-term decisions and daily activity-travel decisions are interdependent and bound together in some ways. This paper develops an integrated model that links long-term decisions like housing, vehicle ownership and short-term activity-travel decisions like time allocation to activity participation, so as to examine the interactions between long-terms decisions (in terms of expenditure tradeoff), as well as investigate household housing and car ownership decisions from the perspective of time allocation. A group decision approach is adopted to differentiate household members’ relative influence on consensual decision-making and capture intra-household interactions. The proposed model is applied to an empirical case study in Beijing to calibrate the parameters, demonstrate its applicability and analyze household members’ interactive decision-making on housing, car ownership and time use over in-home and out-of-home activities. This study will be helpful for gaining a deep understanding of spatial distribution of households, household car ownership levels and individuals’ activity-travel patterns.

Representing Accessibility: Evidence from Vehicle Ownership Choices and Property Valuations in Singapore

This paper compares the relative performance of different measures of accessibility in relevant models. Specifically, the authors formulated three measures of accessibility: gravity-based accessibility, an aggregate measure of potential; trip-based accessibility, a disaggregate, utility-based measure of the value of travel alternatives; and activity-based accessibility, a theoretically richer disaggregate, utility-based measure of the value of alternative activities (including travel). These accessibility measures were used as explanatory variables in household vehicle ownership models and real estate market price models, comparing the explanatory power of each accessibility measure in each model as expressed by the confidence in the coefficient estimates and captured by the models’ goodness-of-fit statistics. It was found that trip-based accessibility best represents preferences for accessibility in both vehicle ownership decisions and property valuations. This supports the theoretical value of disaggregate, utility-based accessibility measures over aggregate, potential-based measures. The fact that trip-based measures perform better than activity-based accessibility measures underscores several empirical and technical limitations. Finally, the authors noted that accurately representing accessibility preferences requires congruence between the granularity of the accessibility measure and that of the explained behavior. This emphasizes the importance of understanding what accessibility measures actually capture and ensuring that they align with the analysis purpose.

Quantifying the explanatory power of mobility-related attributes in explaining vehicle ownership decisions

This paper shows the explanatory power of mobility-related attributes in explaining vehicle ownership decisions. In contrast with the state of the art, in which socio-demographic information is usually employed to explain vehicle ownership decisions, we highlight how the quality of travel offered by a given mode of transportation in serving one's activity pattern influences the ownership of a mode of transport. To implement this approach, we employ a two-level model: first, we estimate the mode choice for performing a set of activities, and second, we use the results of the mode choice calibration to estimate an ownership model. We also show the need to estimate this two-step model, originally modelled as a two-step multinomial logit, with more complex logit structures. With the aim to consider all possible correlations included in each step, a Mixed Logit model and a Cross-nested logit model are employed. Finally, the influence of the socio-demographic information on the quality of travel offered by a given transport mode in performing a particular activity is discussed.

Modeling heterogeneous vehicle ownership in China: A case study based on the Chinese national survey

Understanding the heterogeneity of vehicle ownership decisions in China is vital to accurately estimating the rate of vehicle ownership in its various provinces. In this study, we employ a latent class model to investigate the heterogeneity of vehicle ownership behavior, based on the China Household Finance Survey (CHFS) data. The results show that the households within the CHFS data can be categorized into two classes, and that households within each class rank the importance of socioeconomic variables in significantly different ways. For instance, with regards to deciding to own a vehicle, the households in Class 1 (the income-based class) rank household income as the most important factor, while the households in Class 2 (the comprehensive considerations class) rank household income, household status, and household size as being almost equally important. Further, the model coefficients also reveal the evolution of vehicle ownership in the near future, and how changes in macroeconomic variables may influence household vehicle ownership decisions. In application, the results can be used to assist policy makers in designing policies that control excessively high levels of vehicle ownership; they can also be used to help auto manufacturers pinpoint specific vehicle models to be sold in different regions of China, so as to drive the highest possible profits.

A Piecewise Linear Multinomial Logit Model of Private Vehicle Ownership Behaviour of Indian Households

Development of vehicle ownership models is challenging in developing countries due to lack of quality data. In the Indian context, although some studies have been conducted about disaggregate modelling of vehicle ownership behaviour, most of them are region specific. This paper reviews the development of a basic multinomial logit vehicle ownership model presented earlier by the same authors using available data sets in India and then presents another multinomial logit model which uses a segmented specification of the systematic utilities of various alternatives. The new model is designed to examine if the various factors affecting vehicle ownership behaviour do so differently for richer households as compared to the rest of the households. It is observed that the various factors indeed affect private vehicle ownership decision making process differently for richer households. The new model is then tested for efficiency using a simulation testing technique. The simulation test shows that the new model is effective in modelling private vehicle ownership in Indian scenario. The paper also presents various inferences drawn from the coefficients of the explanatory variables included in the model.

The influences of past and present residential locations on vehicle ownership decisions

This study explores the relationship between historical exposure to the built environment and current vehicle ownership patterns. The influence of past exposure to the built environment on current vehicle ownership decisions may be causal, but there are alternative explanations. Households may primarily select to live in neighborhoods that facilitate their vehicle ownership preferences, or they may retain preferences that they have developed in the past, irrespective of their current situations. This study seeks to control for these alternative explanations by including the built environment attributes of households’ past residences as an influence on vehicle ownership choices. We use a dataset from a credit reporting firm that contains up to nine previous residential ZIP codes for households currently living in the 13-county Atlanta, Georgia, metropolitan area. Results show that past location is significant, but of marginal influence relative to the attributes of the current location. From a practical perspective, our results suggest that models that include current but not past neighborhood attributes (also controlling for standard socioeconomic variables) can forecast vehicle ownership decisions reasonably well. However, models that include both current and past neighborhood attributes can provide a more nuanced understanding of the built environment’s potentially causal influences on vehicle ownership decisions. This better understanding may provide more realistic forecasts of responses to densification or other travel demand management strategies.

Analysis of vehicle ownership evolution in Montreal, Canada using pseudo panel analysis

This paper employs a pseudo-panel approach to study vehicle ownership evolution in Montreal region, Canada using cross-sectional origin–destination survey datasets of 1998, 2003 and 2008. Econometric modeling approaches that simultaneously accommodate the influence of observed and unobserved attributes on the vehicle ownership decision framework are implemented. Specifically, we estimate generalized versions of the ordered response model—including the generalized, scaled- and mixed-generalized ordered logit models. Socio-demographic variables that impact household’s decision to own multiple cars include number of full and part-time working adults, license holders, middle aged adults, retirees, male householders, and presence of children. Increased number of bus stops, longer bus and metro lengths within the household residential location buffer area decrease vehicle fleet size of households. The observed results also varied across years as manifested by the significance of the interaction terms of some of the variables with the time elapsed since 1998 variable. Moreover, variation due to unobserved factors are captured for part-time working adults, number of bus stops, and length of metro lines. In terms of the effect of location of households, we found that some neighborhoods exhibited distinct car ownership temporal dynamics over the years.

Microsimulating Automobile Markets: Evolution of Vehicle Holdings and Vehicle Pricing Dynamics

Vehicle ownership decisions are central to estimates of emissions, gas tax revenues, energy security, pavement management, and other concerns. This work combines an auction-style microsimulation of vehicle prices and random-utility-maximizing choices, producing a market model for the evolution of new and used personal-vehicle fleets. All available vehicles compete directly, with demand, supply, and price signals endogenous to the model. The framework is described, analyzed, and implemented to show its capabilities in predicting outcomes of varying inputs. Application of the model system using Austin, Texas, survey data over a 20-year period highlight the model’s flexibility and reasonable response to multiple inputs, as well as potential implementation issues.

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%).

Modeling interdependencies between vehicle transaction, residential relocation and job change

This paper introduces a vehicle transaction timing model which is conditional on household residential and job relocation timings. Further, the household residential location and members’ job relocation timing decisions are jointly estimated. Some researchers have modeled the household vehicle ownership decision jointly with other household decisions like vehicle type choice or VMT; however, these models were basically static and changes in household taste over time has been ignored in nearly all of these models. The proposed model is a dynamic joint model in which the effects of land-use, economy and disaggregate travel activity attributes on the major household decisions; residential location and members’ job relocation timing decisions for wife and husband of the household, are estimated. Each of these models is estimated using both the Weibull and log-logistic baseline hazard functions to assess the usefulness of a non-monotonic rather than monotonic baseline hazard function. The last three waves of the Puget Sound Panel Survey data and land-use, transportation, and built environment variables from the Seattle Metropolitan Area are used in this study as these waves include useful explanatory variables like household tenure that were not included in the previous waves.

The Built Environment and Motor Vehicle Ownership and Use: Evidence from Santiago de Chile

This paper examines the relationships between the built environment—both ‘neighborhood’ design characteristics and relative location—and motor vehicle ownership and use in a rapidly motorising, developing city context, that of Santiago de Chile. A vehicle choice model suggests that income dominates the household vehicle ownership decision, but also detects a relationship between several built environment characteristics and a household’s likelihood of car ownership. A second model, directly linked to the ownership model to correct for selection bias and endogeneity, suggests a strong relationship with locational characteristics like distance to the central business district and Metro stations. Elasticities of vehicle kilometres travelled (VKT), calculated via the combined models, suggest that income plays the overall largest single role in determining VKT. In combination, however, a range of different design and relative location characteristics also display a relatively strong association with VKT.

INTER-TEMPORAL ANALYSIS OF HOUSEHOLD CAR AND MOTORCYCLE OWNERSHIP BEHAVIORS: The Case in the Nagoya Metropolitan Area of Japan, 1981–2001

This study investigates household car and motorcycle ownership in the Nagoya metropolitan area of Japan. Bivariate ordered probit models of household vehicle ownership were developed using the data from the case study area at 3 time points: 1981, 1991, and 2001. The accessibility that is generally known to be correlated with vehicle ownership decisions is incorporated as an input for the proposed vehicle ownership model to investigate the potential relationship between them. The mode choice models for the area were first estimated to quantify the accessibility indexes that were later integrated into the vehicle ownership models. Inter-temporal comparison and temporal transferability analysis were conducted. Some of the major findings suggest that: 1) age and gender differences have become less important in modal choices and car ownership as motorization proceeds; 2) the accessibility seems to have a significant correlation with vehicle ownership; 3) car and motorcycle ownership may not be independent and may have a complementary relationship; and 4) the deep insights concerning the model selection are obtained from the viewpoints of the temporal transferability.

Modeling Household Vehicle Transaction Behavior

Household vehicle ownership analysis is an important and emerging topic in transportation research. Transportation researchers are interested in examining policy scenarios that can decrease the number of vehicles in a household fleet or encourage alternative transit. Many researchers have studied aspects of vehicle ownership and developed various aggregate and disaggregate models in different frameworks, including hazard-based duration models. Duration models can model the competing nature of vehicle transactions in a household and predict the timing of a change in vehicle ownership. Competing duration models are used to analyze the household vehicle ownership decision. Three vehicle transaction decisions (purchase, trade, and disposal of a vehicle) are considered. Dynamic variables that represent the state of the household and household location characteristics, an exogenous logsum variable representing maximum utility of vehicle class and vintage choice, and time-varying covariates (e.g., number of adults and household income) are considered in the model. Independent hazard models for each outcome and a competing hazard model are developed and compared.