Estimating local car ownership models

Clark, Stephen

doi:10.1016/j.jtrangeo.2006.02.014

Cited by 79 publications

(54 citation statements)

References 15 publications

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“…This covariance is called spatial dependency. Regression analyses, such as OLS, which do not compensate for spatial dependency (Clark, 2007), can have unstable parameter estimates and yield unreliable significance tests (Brunsdon et al, 1996). Moreover, unless a space is uniform and boundless, every location has some degree of uniqueness relative to other locations.…”

Section: Geographically Weighted Regression (Gwr)mentioning

confidence: 99%

Socio-demographic and built environment determinants of car use among older adults in Iran

Soltani

Pojani

Askari

et al. 2018

Journal of Transport Geography

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Internationally, an age-friendly built environment, including safe, affordable, and convenient transportation, has been identified as crucial in enhancing the quality of life for increasingly large numbers of elderly people. This study presents data on the urban travel and mobility patterns of older adults in Shiraz, Iran -a country where the population is ageing faster than the average of western societies. The relationship between car use and sociodemographic and built environment variables is investigated through Ordinary Least Squares and Geographically Weighted Regression models. These models show that the main determinants of car use among older adults are: employment status; household size; car ownership rate; trip purpose; property prices; and mixed land-uses. Also, the study reveals that car dependency is significant and more pronounced among wealthier individuals, who tend to be clustered in the northern section of the city. The population density and road network design characteristics do not affect car use among older adults, while a mix of land-uses tends to attenuate car dependency. Gender gaps in car use are much wider than in the West, and some level of spatial mismatch between travel needs and car access is observed in the southern section of the city. Clearly, Iranian cities must move away from the current car-centric development model.

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Section: Geographically Weighted Regression (Gwr)mentioning

confidence: 99%

Socio-demographic and built environment determinants of car use among older adults in Iran

Soltani

Pojani

Askari

et al. 2018

Journal of Transport Geography

View full text Add to dashboard Cite

show abstract

“…e GWR approach has recently found use in various applications. ere are examples in the elds of climatology , ecology (Kimsey et al 2008;Zhang and Shi 2004), education (Fotheringham et al 2001), marketing research (Mittal et al 2004), regional science (Huang and Leung 2002), political science (Calvo and Escolar 2003), and transport research (Chow et al 2006;Clark 2007;Hadayeghi et al 2003;Lloyd and Shuttleworth 2005;Nakaya 2001). In the housing eld, there are studies by Bitter et al (2007), Farber and Yeates (2006), Fotheringham et al (2002), Kestens et al (2006), Páez et al (2007), and Yu et al (2007).…”

Section: Spatial Simultaneous Autoregressive Models and Geographicallmentioning

confidence: 99%

“…Consequently, comparisons of the performance of different measures, which take into account spatial effects, have begun to emerge in the literature. Clark (2007) applied a spatial error model and GWR for estimating local car ownership in the United Kingdom, but did not focus on comparing the results in particular. Wall (2004) compared conditional autoregressive models (CAR) and simultaneously autoregressive models, but found possible counter-intuitive or impractical results.…”

Section: Comparative Studies In the Literaturementioning

confidence: 99%

Modelling hedonic residential rents for land use and transport simulation while considering spatial effects

Löchl

Axhausen

2010

JTLU

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Abstract:e application of UrbanSim requires land or real estate price data for the study area. ese can be difficult to obtain, particularly when tax assessor data and data from commercial sources are unavailable. e article discusses an alternative method of data acquisition and applies hedonic modeling techniques in order to generate the required data. Many studies have highlighted that ordinary least square (OLS) regression approaches lack the ability to consider spatial dependency and spatial heterogeneity, consequently leading to biased and inefficient estimations. erefore, a comprehensive data set is used for modeling residential asking rents by applying and comparing OLS, spatial autoregressive, and geographically weighted regression (GWR) techniques. e latter technique performed best with regard to model t, but the issue of correlated coefficients favored a spatial simultaneous autoregressive model. Overall, the article reveals that when housing markets are a particular concern in UrbanSim applications, signi cant efforts are needed for the price data generation and modeling. e study concludes with further development potentials for UrbanSim.

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“…However, because of non-linear structure of car ownership and local variation review, this method does not provide desired results. Accordingly, Geographically Weighted Regression model was used (Clark, 2007). Time Series aggregate models, usually by using the S-shaped curve and the saturation level of vehicle ownership concept, developed over a period of time (Button et al, 1993;Ogut, 2004).…”

Section: Car Ownership Modelling In the International Contextmentioning

confidence: 99%

Car Ownership Models in Iran: A Review of Methods and Determinants

Shaygan

Mamdoohi

Masoumi

2017

Transport and Telecommunication Journal

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The achievement of sustainable transportation and the desire to reduce car ownership are important aspects of urban planning, especially in developing countries. This paper presents an overview of researches and studies conducted on car ownership to find the knowledge gap and neglected modelling methods that should be employed in the further researches in Iran as one of the Middle East countries. It is found that car ownership models have under gone modification and improvement from regression models towards discrete choice models, although more improvement is necessary and underway. Lack of reliable aggregate and disaggregate data regarding public transport, land use, behavioural and socioeconomic variables particularly for small cities is prominent. To fill the gap, more descriptive and analytical researches and more complex modelling methods and variables need be delved.

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Estimating local car ownership models

Cited by 79 publications

References 15 publications

Socio-demographic and built environment determinants of car use among older adults in Iran

Socio-demographic and built environment determinants of car use among older adults in Iran

Modelling hedonic residential rents for land use and transport simulation while considering spatial effects

Car Ownership Models in Iran: A Review of Methods and Determinants

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