Volatility of car ownership, commutingmode and time in the UK

This paper has two objectives: to examine the volatility of travel behaviour over timeand consider the factors explaining this volatility; and to estimate the factors determiningcar ownership and commuting by car. The analysis is based on observations of individualsand households over a period of up to eleven years obtained from the British HouseholdPanel Survey (BHPS). Changes in car ownership, commuting mode and commuting timeover a period of years for the same individuals/households are examined to determine theextent to which these change from year to year. This volatility of individual behaviour is ameasure of the ease of change or adaptation. If behaviour changes easily, policy measuresare likely to have a stronger and more rapid effect than if there is more resistance tochange. The changes are ?explained? in terms of factors such as moving house, changingjob and employment status. The factors determining car ownership and commuting by carare analysed using a dynamic panel-data models

Land use and mobility

This paper analyses the effects of land use characteristics on mode choice and carownership. The study is based on a large sample of individuals from the National TravelSurvey of Great Britain for the years 1989-91 and 1999-2001. Land use characteristics aredefined as population density, size of the municipality, accessibility to public transport andlocal amenities, such as shops and services. Mode choice (shares of total travel by car,public transport and walking) and car ownership are modelled using multinomial andbinomial logit models respectively, which include a large number of socio-economicfactors (income, age, gender, household structure and employment status) as well as landuse indicators. The estimation results strongly support the importance of the land usefactors considered on mode choice and car ownership

Life events and travel behavior exploring the interrelationship using UK Household Longitudinal Study data

Recent research has indicated that changes in travel behavior are more likely at the time of major life events. However, much remains to be learned about the extent to which different life events trigger behavioral change and the conditions under which life events are more likely to trigger change. The UK Household Longitudinal Study (UKHLS) offers a previously unavailable opportunity to investigate this topic for a large, representative sample of the UK population. UKHLS data were also linked to local spatial data drawn from the census and other sources to elucidate the effect of the spatial context on changes to travel behavior in association with life events. Findings from an exploratory analysis of data from UKHLS Waves 1 and 2 are presented first Transition tables demonstrate a strong association between changes in car ownership and commute mode and the following life events: employment changes, residential relocation, retirement, the birth of children, and changes in household structure. The results of logit models that relate the probability of an increase and a decrease in the number of cars owned to the occurrence of life events and that control for individual and household characteristics and spatial context are then shown. These models show, for example, that moves to urban and rural areas have contrasting effects on travel behavior and that having a new child in itself is not a significant influence on car ownership in the short term

Conceptual model to explain turning points in travel behavior: Application to bicycle use

Existing knowledge on cycling behavior, as with travel behavior in general, is based mainly on cross-sectional studies. It is questionable how much can be learned about the reasons for behavioral change from such studies. A major investment program to promote cycling in 12 English cities and towns between 2008 and 2011 provided the opportunity to study the bicycle use of residents and how that use was affected by the investment. Face-to-face interviews collected biographical information on travel behavior and life-change events during the investment period for 144 research participants and probed the reasons for changes in bicycle use. Theory (from the life course perspective) and preliminary analysis of the interview data were used to develop a conceptual model that hypothesized that turning points in travel behavior were triggered by contextual change (a life-change event or change in the external environment) and mediated by intrinsic motivations, facilitating conditions, and personal history. The model provided an effective means of explaining turning points in bicycle use. The analysis of the interview data showed how the nature of behavioral influences (in particular, life-change events and intrinsic motivations) varied over the life course. The research highlights the advantages of viewing travel behavior change in the context of people’s evolving lives and how that approach can help in developing transport policies and practices

Access to recreational physical activities by car and bus : an assessment of socio-spatial inequalities in mainland Scotland

Obesity and other chronic conditions linked with low levels of physical activity (PA) are associated with deprivation. One reason for this could be that it is more difficult for low-income groups to access recreational PA facilities such as swimming pools and sports centres than high-income groups. In this paper, we explore the distribution of access to PA facilities by car and bus across mainland Scotland by income deprivation at datazone level. GIS car and bus networks were created to determine the number of PA facilities accessible within travel times of 10, 20 and 30 minutes. Multilevel negative binomial regression models were then used to investigate the distribution of the number of accessible facilities, adjusting for datazone population size and local authority. Access to PA facilities by car was significantly (p<0.01) higher for the most affluent quintile of area-based income deprivation than for most other quintiles in small towns and all other quintiles in rural areas. Accessibility by bus was significantly lower for the most affluent quintile than for other quintiles in urban areas and small towns, but not in rural areas. Overall, we found that the most disadvantaged groups were those without access to a car and living in the most affluent areas or in rural areas

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