Changes in level of household car ownership: the role of life events and spatial context

Recent longitudinal studies of household car ownership have examined factors associated with increases and decreases in car ownership level. The contribution of this panel data analysis is to identify the predictors of different types of car ownership level change (zero to one car, one to two cars and vice versa) and demonstrate that these are quite different in nature. The study develops a large scale data set (n = 19,334), drawing on the first two waves (2009–2011) of the UK Household Longitudinal Study (UKHLS). This has enabled the generation of a comprehensive set of life event and spatial context variables. Changes to composition of households (people arriving and leaving) and to driving licence availability are the strongest predictors of car ownership level changes, followed by employment status and income changes. Households were found to be more likely to relinquish cars in association with an income reduction than they were to acquire cars in association with an income gain. This may be attributed to the economic recession of the time. The effect of having children differs according to car ownership state with it increasing the probability of acquiring a car for non-car owners and increasing the probability of relinquishing a car for two car owners. Sensitivity to spatial context is demonstrated by poorer access to public transport predicting higher probability of a non-car owning household acquiring a car and lower probability of a one-car owning household relinquishing a car. While previous panel studies have had to rely on comparatively small samples, the large scale nature of the UKHLS has provided robust and comprehensive evidence of the factors that determine different car ownership level changes.     

From e-bike to car: A study on factors influencing motorization of e-bike users across China

Household car ownership has risen dramatically in China over the past decade. At the same time a disruptive transportation technology emerged, the electric bike (e-bike). Most studies investigating motorization in China focus on macro-level economic indicators like GDP, with few focusing on household, city-level, environmental, or geographic indicators, and none in the context of high e-bike ownership. This study examines household vehicle purchase decisions across 59 cities in China with broad geographic, environmental, and socio-economic characteristics. We focus on a subset of households who own e-bikes and rely on a telephone survey from an industry customer database. From these responses, we estimate two three-level hierarchical choice models to assess attributes that contribute to (1) recent car purchases and (2) the intention to buy a car in the near future. The results show that the models are dominated by household characteristics including household income, household size, household vehicle ownership, number of licensed drivers and duration of car ownership. Some geographic, environmental and socio-economic factors have significant influences on car purchase decisions. Only two city-level transportation variable have an effect – higher taxi density and higher bus density reducing car purchase. Cold weather, population density gross domestic product per capita positively influence car purchase, while urbanization rate reduces car purchase. Because of supply heterogeneity in the data set, described by publicly available urban transportation data, this is the first study that can include geographic and urban infrastructure differences that influence purchase choice and suggests potential region-specific policy approaches to managing car purchase may be necessary.     

Job accessibility and employment outcomes: which income groups benefit the most?

Improving job accessibility can increase the probability for individual persons to be employed and reduce their commutes. Empirical research suggests that the relationship between job accessibility and employment outcomes differ across income groups, but no research has investigated the difference or explored which income groups benefit the most from job accessibility improvements. This research fills the gap by examining six income groups in the Los Angeles metropolitan area. Results show that job accessibility affects the employment status of medium-to-low income groups (household income between US$25,000 and US$75,000). For the lowest-income group (household income lower than US$25,000), owning a car significantly improves their chances to be employed, but job accessibility has no effect. On the other hand, higher job accessibility is associated with shorter commuting distance for the other five income groups, but not for the lowest-income group. These results suggest that transportation and land use policies need to address the specific needs of distinct population groups and underscore the importance of spatial access for the middle-class, which tends to be overlooked in the literature on transportation equity.     

Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal

This paper addresses the relations between travel behavior and land use patterns using a Structural Equations Modeling (SEM) framework. The proposed model structure draws on two earlier models developed for Lisbon and Seattle which show significant effects of land use patterns on travel behavior. The travel behavior variables included here are multifaceted including commuting distance, car ownership, the amount of mobility by mode (car, transit and non-motorized modes), both in terms of total kilometers travelled and number of trips. The model also includes a travel scheduling variable, which is the total time spent between the first and last trips to reflect daily constraints in time allocation and travel.The modeled land use variables measure the levels of urban concentration and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants/residents, the transport supply levels, transit and road infrastructure, and accessibility indicators. The land use patterns are described both at the residence and employment zones of each individual included in the model by using a factor analysis technique as a data reduction and multicollinearity elimination technique. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The results obtained show that people with different socioeconomic characteristics tend to work and live in places of substantially different urban environments. But besides these socioeconomic self-selection effects, land use variables significantly affect travel behavior. More precisely the effects of land use are in great part passed thru variables describing long term decisions like commuting distance, and car ownership. These results point to similar conclusions from the models developed for Lisbon and Seattle and thus give weight to the use of land use policies as tools for changing travel behavior.     

Changing household car ownership level and life cycle events: an action in anticipation or an action on occurrence

This paper analyzes households’ decision to change their car ownership level in response to actions/decisions regarding mobility issues and other household events. Following recent literature on the importance of critical events for mobility decisions, it focuses on the relationship between specific events (e.g. childbirth and buying an extra car), rather than trying to explain the status of car ownership from a set of stationary explanatory variables. In particular, it is hypothesized that changes in household car ownership level take place in response to stressors, resulting from changed household needs or aspirations. The study includes a broad range of events. Apart from changes in work status, employer and residential location, it analyzes demographic events such as household formation and childbirth. Also, it scrutinizes the temporal sequence in which chains of related events are most likely to occur. To this end, data from a retrospective survey that records respondents’ car ownership status, as well as residential and household situation over the past 20 years are used. A panel analysis has been carried out to disentangle typical relationships. The results suggest that strong and simultaneous relationships exist between car ownership changes and household formation and dissolution processes. Childbirth and residential relocation invoke car ownership changes. Changes are also made in anticipation of future events such as employer change and childbirth. Childbirth is associated with increasing the number of cars, whereas the effect of employer change goes the opposite way. Job change increases the probability of car ownership change in the following year.     

Volatility of car ownership, commuting mode and time in the UK

This paper has two objectives: to examine the volatility of travel behaviour over time and consider the factors explaining this volatility; and to estimate the factors determining car ownership and commuting by car. The analysis is based on observations of individuals and households over a period of up to 11 years obtained from the British Household Panel Survey (BHPS). Changes in car ownership, commuting mode and commuting time over a period of years for the same individuals/households are examined to determine the extent to which these change from year-to-year. This volatility of individual behaviour is a measure of the ease of change or adaptation. If behaviour changes easily, policy measures are likely to have a stronger and more rapid effect than if there is more resistance to change. The changes are “explained” in terms of factors such as moving house, changing job and employment status. The factors determining car ownership and commuting by car are analysed using a dynamic panel-data models.     

A causal analysis of car ownership and transit use

The causal structure underlying household mobility is examined in this study using a sample obtained from the Dutch National Mobility Panel survey. The results indicate that car ownership is strongly associated with mode use, but that it has no influence on weekly person trip generation by household members. Characteristics of mode use are examined through a causal analysis of changes in car ownership, number of drivers, number of car trips, and number of transit trips. It is shown that observed changes in mode use cannot be adequately explained by assuming that a change in transit use influences car use. The finding suggests that the increase in car use, which is a consequence of increasing car ownership, may not be suppressed by improving public transit.     

A dynamic analysis of household car ownership

This paper examines the determinants of household car ownership, using Irish longitudinal data for the period 1995–2001. This was a period of rapid economic and social change in Ireland, with the proportion of households with one or more cars growing from 74.6% to 80.8%. Understanding the determinants of household car ownership, a key determinant of household travel behaviour more generally, is particularly important in the context of current policy developments which seek to encourage more sustainable means of travel. In this paper, we use longitudinal data to estimate dynamic models of household car ownership, controlling for unobserved heterogeneity and state dependence. We find income and previous car ownership to be the strongest determinants of differences in household car ownership, with the effect of permanent income having a stronger and more significant effect on the probability of household car ownership than current income. In addition, income elasticities differ by previous car ownership status, with income elasticities higher for those households with no car in the initial period. Other important influences include household composition (in particular, the presence of young children) and lifecycle effects, which create challenges for policymakers in seeking to change travel behaviour.     

Household-level commuting mode choices,car allocation and car ownership level choices of two-worker households: the case of the city of Toronto

The paper presents a comprehensive investigation on household level commuting mode, car allocation and car ownership level choices of two-worker households in the City of Toronto. A joint econometric model and a household travel survey dataset are used for empirical investigations. Empirical models reveal that significant substitution patterns exist between auto driving and all other mode choices in two-worker households. It is revealed that, female commuters do not prefer auto driving, but in case of a one car (and two commuters with driving licenses) household, a female commuter gets more preference for auto driving option than the male commuter. Reverse commuting (commuting in opposite direction of home to central business district) plays a critical role on household level car allocation choices and in defining the stability of commuting behaviour of two-worker households. Two worker households in higher income zones and with longer commuting distances tend to have higher car ownership levels than others. However, higher transit accessibility to jobs reduces household car ownership levels. The study reveals that both increasing two worker households and reverse commuting would increase dependency on private car for commuting.     

A dynamic model system of household car ownership,trip generation,and modal split: model development and simulation experiment

A dynamic model of household car ownership and mode use is developed and applied to demand forecasting. The model system consists of three interrelated components: car ownership, mechanized trip generation, and modal split. The level of household car ownership is represented as a function of household attributes and mobility measures from the preceding observation time point using an ordered-response probit model. The trip generation model predicts the weekly number of trips made by household members using car or public transit, and the modal split model predicts the fraction of trips that are made by public transit. Household car ownership is a major determinant in the latter two model components. A simulation experiment is conducted using sample households from the Dutch National Mobility Panel data set and applying the model system to predict household car ownership and mode use under different scenarios on future household income, employment, and drivers’ license holding. Policy implications of the simulation results are discussed.     

The asymmetric income effect on household vehicle ownership in Taiwan: A threshold cointegration approach

This paper uses the asymmetric threshold cointegration test to examine the asymmetric relationship between household income and vehicle ownership in Taiwan, presenting estimated asymmetric error correction models. The empirical data include information on household income, car ownership and motorcycle ownership in different regions from 1974 to 2009. The results show that, first, motorcycle ownership is asymmetrically cointegrated with household income in each region, and car ownership is asymmetrically cointegrated with household income in all regions except Taipei city. Second, both car and motorcycle ownership levels increase faster than they decrease in the asymmetric adjustment of their long-run relationship. Third, sensitivity tests for the period 1987-2009 show that the cointegration relationship of the car ownership equations vanished. Finally, we find evidence on the effects of household income on motorcycle ownership, and the effects of income variables on car and motorcycle ownership are dissimilar. This study exhibits different results across regions. These findings may be related to the development of public transit system in each region.     

A study on the determinants of private car ownership in China: Findings from the panel data

This study examines the determinants of private car ownership in China. The target cities are 32 provincial capital cities and the target period is from 2001 to 2011. In order to capture the individual effects (heterogeneity), the fixed and random effect models are adopted and compared, in which 8 explanatory variables are selected to include economic characteristics, urban characteristics, and transportation characteristics. Moreover, double natural logarithm model is employed to measure the elastic relationship between the private car ownership and regressors. The estimated results show that the fixed effect model performs better than pooled regression model and the random effect model. In addition, there are variations of private car ownership among cities and regions. Finally, the influence of factors responsible for these variations is also presented and discussed in this paper.     

Does transport help people to gain employment? A systematic review and meta-analysis of the empirical evidence

ABSTRACT

The role of transport in providing access to employment has received considerable attention. Since transport policies may be motivated by assumed effects on employment probability outcomes, it is important to establish the nature of the relationship between transport and employment outcomes. While the majority of the empirical evidence suggests a positive association, it is not conclusive or consistent and often shows mixed results. To address this confusion, our study has systematically reviewed this evidence base and synthesised it through meta-analysis. We first identified 93 studies that quantitatively assessed the impact of transport on employment outcomes. By systematically merging the empirical evidence, this study establishes a positive association between transport and employment outcomes, with varying effects for four identified categories of transport measures (or combinations thereof): car ownership, public transport access, commute times, and job accessibility levels. This positive association persists in studies that control for endogeneity between transport and employment, but a larger evidence base is needed to establish a more robust relationship, in particular for cities and smaller (rural) areas outside the US-context and with regard to public transport. We then selected 20 methodologically comparable studies for inclusion in the meta-analysis. Our meta-regression models clearly demonstrate that car ownership significantly increases individual employment probabilities, in particular among welfare recipients. Young drivers benefit from access to household cars when these are not in use by their parents, and they are more sensitive to the time and cost implications of longer commutes. While our systematic review suggests that better access to public transport and higher levels of job accessibility increases employment probabilities, meta-regression analysis requires more consistent transport measures. The findings in this study are important for policymakers in that they imply that job seekers may benefit from public policies targeted at improving their access to public transport, in particular for people without access to cars and in areas with fewer job opportunities.     

Car ownership,travel and land use: a comparison of the US and Great Britain

This paper conducts an international comparative analysis of relationships between car ownership, daily travel and urban form. Using travel diary data for the US and Great Britain, we estimate models of car ownership and daily travel distance. Both a structural model with daily travel conditional upon car ownership and a reduced form model for daily travel, excluding car ownership, are estimated. Model results are similar, and show that differences in travel are explained by (1) differences in demographics between the two countries; (2) lower household income in Great Britain; (3) country specific differences in costs of car ownership and use, transport supply and other factors we have not been able to control. We find that metropolitan size affects travel only in the largest metropolitan areas of the US. Daily travel distance is inversely related to local population density, but the effect is much stronger for the US than Great Britain. We conclude that higher transport costs in Great Britain promote economizing behavior, which in turns leads to more consumption of local goods and services and more use of alternative transport modes.     

The effect of income on car ownership: evidence of asymmetry

This paper examines the effect of income on car ownership, and specifically the question of hysteresis or asymmetry. Although there is little doubt that rising income leads to higher car ownership, less is understood about the effect of falling income. Traditional demand modelling is based on the implicit assumption that demand responds symmetrically to rising and falling income. The object of this study is to test this assumption statistically. Using a dynamic econometric model relating household car ownership to income, the number of adults and children in the household, car prices and lagged car ownership, income decomposition techniques are employed to separately estimate elasticities with respect to rising and falling income. The equality of these elasticities – no hysteresis – is tested statistically against the inequality – hysteresis – hypothesis. Various functional specifications are tested in order to assure the robustness of the results to assumptions concerning functional form. The estimation is based on cohort data constructed from 1970 to 1995 UK Family Expenditure Surveys, and a pseudo-panel methodology is employed. The results indicate that car ownership responds more strongly to rising than to falling income – there is a ‘stickiness’ in the downward direction. In addition, there is evidence that the income elasticity is not constant, but instead declines with increasing car ownership.     

Quantifying the relative contribution of factors to household vehicle miles of travel

Household vehicle miles of travel (VMT) has been exhibiting a steady growth in post-recession years in the United States and has reached record levels in 2017. With transportation accounting for 27 percent of greenhouse gas emissions, planning professionals are increasingly seeking ways to curb vehicular travel to advance sustainable, vibrant, and healthy communities. Although there is considerable understanding of the various factors that influence household vehicular travel, there is little knowledge of their relative contribution to explaining variance in household VMT. This paper presents a holistic analysis to identify the relative contribution of socio-economic and demographic characteristics, built environment attributes, residential self-selection effects, and social and spatial dependency effects in explaining household VMT production. The modeling framework employs a simultaneous equations model of residential location (density) choice and household VMT generation. The analysis is performed using household travel survey data from the New York metropolitan region. Model results showed insignificant spatial dependency effects, with socio-demographic variables explaining 33 percent, density (as a key measure of built environment attributes) explaining 12 percent, and self-selection effects explaining 11 percent of the total variance in the logarithm of household VMT. The remaining 44 percent remains unexplained and attributable to omitted variables and unobserved idiosyncratic factors, calling for further research in this domain to better understand the relative contribution of various drivers of household VMT.     

TTB or not TTB, that is the question: a review and analysis of the empirical literature on travel time (and money) budgets

This paper summarizes and analyses findings from more than two dozen aggregate and disaggregate studies of travel time (and sometimes money) expenditures, exploring the question of the existence of a constant travel time budget. We conclude (with prior researchers) that travel time expenditures are not constant except, perhaps, at the most aggregate level. Nevertheless, individuals’ travel time expenditures do show patterns that can be partly explained by measurable characteristics. Travel time expenditure is strongly related to individual and household characteristics (e.g., income level, gender, employment status, and car ownership), attributes of activities at the destination (e.g., activity group and activity duration), and characteristics of residential areas (e.g., density, spatial structure, and level of service). To the extent that travel time expenditures are constant at the aggregate level, the underlying mechanisms explaining that regularity are not well understood. Consequently, further research into explaining travel time and money expenditure patterns is justified.     

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.     

A repeated cross-sectional evaluation of car ownership

This paper studies changes in the relationship between household car ownership and income by household type. Ordered response probit models of car ownership are estimated for a sample of households repeatedly at six time points to track the evolution of income elasticities of car ownership over time. Elasticities of car ownership are found to change over time, questioning the existence of a unique equilibrium point between demand and supply that is implicitly assumed in traditional cross-sectional discrete choice car ownership models. Moreover, different household types and households that underwent household type transitions showed differing patterns of change in elasticities. Observed trends in car ownership and income clearly show behavioral asymmetry where the elasticity of procuring an additional car is greater than that of disposing a car. This too shows the inadequacy of traditional cross-sectional models of car ownership which tend to predict symmetry in behavior. The study suggests the importance of incorporating dynamic trends into the forecasting process, which can be accomplished through the use of longitudinal data.     

Maximum car ownership under constraints of road capacity and parking space

This paper presents a model for determining the maximum number of cars by zones in view of the capacity of the road network and the number of parking spaces available. In other words, the proposed model is to examine whether existing road network and parking supply is capable of accommodating future zonal car ownership growth (or the reserve capacity in each zone); i.e. the potential maximum zonal car ownership growth that generates the road traffic within the network capacity and parking space constraints. In the proposed model, the vehicular trip production and attraction are dependent on the car ownership, available parking spaces and the accessibility measures by traffic zones. The model is formulated as a bi-level programming problem. The lower-level problem is an equilibrium trip distribution/assignment problem, while the upper-level problem is to maximize the sum of zonal car ownership by considering travellers’ route and destination choice behaviour and satisfying the network capacity and parking space constraints. A sensitivity analysis based heuristic algorithm is developed to solve the proposed bi-level car ownership problem and is illustrated with a numerical example.