Detangling the impacts of age, residential locations and household lifecycle in car usage and ownership in the Osaka metropolitan area, Japan

Sun, Yilin; Susilo, Yusak O.; Waygood, E. Owen D.; Wang, Dianhai

doi:10.1631/jzus.a1300345

Cited by 8 publications

(8 citation statements)

References 22 publications

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“…This also suggests that interventions improving access and level of service of public transportation and accessibility to local opportunities could lead to lower levels of car need and therefore ownership. This agrees with past research indicating that households with fewer vehicles tend to travel less by car in both trips and travel distances (e.g., Sun et al [ 4 ], Sioui et al [ 7 ]), supporting the idea that reducing the need for car ownership could lead to reduced car use.…”

Section: Discussionsupporting

confidence: 92%

“…Car ownership has been shown to relate to personal and built environment characteristics, and in some cases policy (14). Age is found to influence car ownership, but not in a linear manner (4) and some cohorts seem more caroriented (15). When modeled at the household level, income is a key determinant in car ownership, with higher income associated with more cars owned even when neighborhood income variables are included instead of household level income (16).…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is also linked to negative health impacts through road fatalities and injuries, air pollution and stress ( 2 , 3 ). Car ownership itself is not necessarily perceived as an issue despite the extensive evidence that it is positively associated with higher use of cars and reduced use of other modes, see, for example, Sun et al ( 4 ), Eluru et al ( 5 ), Dieleman et al ( 6 ), Sioui et al ( 7 ).…”

mentioning

confidence: 99%

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Car Ownership and the Built Environment: A Spatial Modeling Approach

Laviolette

Morency

Waygood

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Car ownership is linked to higher car use, which leads to important environmental, social and health consequences. As car ownership keeps increasing in most countries, it remains relevant to examine what factors and policies can help contain this growth. This paper uses an advanced spatial econometric modeling framework to investigate spatial dependences in household car ownership rates measured at fine geographical scales using administrative data of registered vehicles and census data of household counts for the Island of Montreal, Canada. The use of a finer level of spatial resolution allows for the use of more explanatory variables than previous aggregate models of car ownership. Theoretical considerations and formal testing suggested the choice of the Spatial Durbin Error Model (SDEM) as an appropriate modeling option. The final model specification includes sociodemographic and built environment variables supported by theory and achieves a Nagelkerke pseudo-R2 of 0.93. Despite the inclusion of those variables the spatial linear models with and without lagged explanatory variables still exhibit residual spatial dependence. This indicates the presence of unobserved autocorrelated factors influencing car ownership rates. Model results indicate that sociodemographic variables explain much of the variance, but that built environment characteristics, including transit level of service and local commercial accessibility (e.g., to grocery stores) are strongly and negatively associated with neighborhood car ownership rates. Comparison of estimates between the SDEM and a non-spatial model indicates that failing to control for spatial dependence leads to an overestimation of the strength of the direct influence of built environment variables.

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Section: Discussionsupporting

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

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Car Ownership and the Built Environment: A Spatial Modeling Approach

Laviolette

Morency

Waygood

et al. 2021

Transportation Research Record: Journal of the Transportation R

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“…In that study, distances by non-motorized modes increase from the most urbanized to the least, and non-motorized modes represent a high of 59% in the most urbanized down to 25% in the towns (it was 32% in the rural/unurbanized). However, there has been a general spreading out of the built environment over the past decades [ 50 ] that is likely linked to more car-oriented development [ 58 , 59 ] resulting in increasing trip distances, which could lead to a lower active travel if those trips are made by motorized modes (though the contribution of active travel to public transport trips is not known). As well, no clear influence of car ownership was found to increase the number of trips (a proxy for activity engagement), but not the average duration of trips when the built environment and lifecycle stage were taken into consideration [ 60 ].…”

Section: Methodsmentioning

confidence: 99%

Active Travel by Built Environment and Lifecycle Stage: Case Study of Osaka Metropolitan Area

Waygood

Sun

Letarte

2015

IJERPH

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Active travel can contribute to physical activity achieved over a day. Previous studies have examined active travel associated with trips in various western countries, but few studies have examined this question for the Asian context. Japan has high levels of cycling, walking and public transport, similar to The Netherlands. Most studies have focused either on children or on adults separately, however, having children in a household will change the travel needs and wants of that household. Thus, here a household lifecycle stage approach is applied. Further, unlike many previous studies, the active travel related to public transport is included. Lastly, further to examining whether the built environment has an influence on the accumulation of active travel minutes, a binary logistic regression examines the built environment’s influence on the World Health Organization’s recommendations of physical activity. The findings suggest that there is a clear distinction between the urbanized centers and the surrounding towns and unurbanized areas. Further, active travel related to public transport trips is larger than pure walking trips. Females and children are more likely to achieve the WHO recommendations. Finally, car ownership is a strong negative influence.

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“…However, this result must be interpreted with care as the working age in Ghana ranges from 18 to 60 and the effect might be different for those over the age of 60 as has been found in other studies. In the UK, Japan, and the Netherlands, researchers found that increasing age increases the likelihood owning a vehicle only up to the age of 50-60; thereafter, the effect decreases (47,51,52).…”

Section: Mnl Estimation Results and Discussionmentioning

confidence: 99%

Determinants of Vehicle Ownership in the Greater Tamale Area, Ghana

Zambang

Jiang

Wahab

2020

Transportation Research Record: Journal of the Transportation R

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Existing models of vehicle ownership in most developing countries, such as Ghana, are aggregate models which explain the factors that contribute to the rise in individual vehicle ownership. In fact, disaggregate models would be a better approach than aggregate models, but the latter is often adopted as a result of inadequate data on individual vehicle ownership. This study attempts to fill this gap by developing a disaggregate model to analyze the factors influencing the rise of private vehicle ownership (motorcycles and cars) in Ghana using the Greater Tamale Area (GTA) as the case study. We approach this by using recently collected data on a cross-section of workers within the city with average monthly incomes, distances to work, perceptions of the provision of non-motorized infrastructure, and sociodemographic factors as the explanatory variables. The findings show that a higher average monthly income, a greater traveling distance to work, a perception that the provision of non-motorized infrastructure is inadequate, increasing age, being married, and being male correlate with a higher likelihood of owning a car or a motorcycle, while living within 2 km of the Central Business District (CBD) correlates with a lower likelihood of car ownership but a higher likelihood of motorcycle ownership. The determination of these factors will help the government to develop policies that will improve mobility but reduce private vehicle dependency.

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Detangling the impacts of age, residential locations and household lifecycle in car usage and ownership in the Osaka metropolitan area, Japan

Cited by 8 publications

References 22 publications

Car Ownership and the Built Environment: A Spatial Modeling Approach

Car Ownership and the Built Environment: A Spatial Modeling Approach

Active Travel by Built Environment and Lifecycle Stage: Case Study of Osaka Metropolitan Area

Determinants of Vehicle Ownership in the Greater Tamale Area, Ghana

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