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Estimating the number of cars in UK and US householdsLawal, Temitope A. January 2021 (has links)
The quest towards resolving concerns about transportation energy consumption
and emissions across nations has created more interests to investigate factors
responsible for households’ car ownership. While literature holds an extensive
body of investigation usually compartmentalised in individual different
disciplines, limited efforts have been made to promote inter-linkages of this
strand of research across different disciplines. To fill this gap, this study
developed an integrating Multinomial logit (MNL) model to examine the impact of
some rarely-investigated and conventional explanatory variables, including:
ethnicity, accommodation tenure, settlement nature, mental belief,
environmental concern, geographical regions, household structure, driving
licence, number of household income earners and household income, on car
ownership.
Analysis based on rich data sets of British Household Survey and US Consumer
Expenditure Survey found not only the conventional explanatory variables to be
significantly linked to the number of cars in the US and UK households, but also
the rarely-investigated psychological variables were found to be significantly
linked as well. As Socio-demography, Geography and Psychology impact on
how people and households process information and assess market offers (e.g.,
products and services), this study presents findings which have beneficial implications for policymakers and transportations planners, including those who
would like to alter people’s behaviour from private car ownership to public
transportation use, car sellers in terms of how to identify and reach potential
customers, provision of alternative forecasting approaches to car ownership
scholars as well as possible consideration for general car ownership decision
making. Caution should be taken when interpreting the relationship between
psychological factors and car ownership since the psychological factors adopted
are measure representatives from databases used with limitations in the factor
structure for a representative sample of the countries’ population.
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GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling / GIS-based Episode Reconstruction Using GPS DataDalumpines, Ron 26 September 2014 (has links)
Most transportation problems arise from individual travel decisions. In response, transportation researchers had been studying individual travel behavior – a growing trend that requires activity data at individual level. Global positioning systems (GPS) and geographical information systems (GIS) have been used to capture and process individual activity data, from determining activity locations to mapping routes to these locations. Potential applications of GPS data seem limitless but our tools and methods to make these data usable lags behind. In response to this need, this dissertation presents a GIS-based toolkit to automatically extract activity episodes from GPS data and derive information related to these episodes from additional data (e.g., road network, land use).
The major emphasis of this dissertation is the development of a toolkit for extracting information associated with movements of individuals from GPS data. To be effective, the toolkit has been developed around three design principles: transferability, modularity, and scalability. Two substantive chapters focus on selected components of the toolkit (map-matching, mode detection); another for the entire toolkit. Final substantive chapter demonstrates the toolkit’s potential by comparing route choice models of work and shop trips using inputs generated by the toolkit.
There are several tools and methods that capitalize on GPS data, developed within different problem domains. This dissertation contributes to that repository of tools and methods by presenting a suite of tools that can extract all possible information that can be derived from GPS data. Unlike existing tools cited in the transportation literature, the toolkit has been designed to be complete (covers preprocessing up to extracting route attributes), and can work with GPS data alone or in combination with additional data. Moreover, this dissertation contributes to our understanding of route choice decisions for work and shop trips by looking into the combined effects of route attributes and individual characteristics. / Dissertation / Doctor of Philosophy (PhD)
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