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UBC Theses and Dissertations
Multicollinearity in transportation models Chan , Sheung-Ling
Abstract
This thesis explores some of the limitations and implications of using multiple regression analysis in transportation models. Specifically it investigates how the problem of multicollinearity, which results from using intercorrelated variables in trip generation models, adversely affects the validation of hypotheses, discovery of underlying relationships and prediction. The research methodology consists of a review of the literature on trip generation analysis and a theoretical exposition on multicollinearity. Secondly, trip generation data for Greater Vancouver (1968) is used for empirical analysis. Factor analysis and multiple regression techniques are employed. The results demonstrate that multicollinearity is both an explanatory and prediction problem which can be overcome by a combined factor analytic and regression method. This method is also capable of identifying and incorporating causal relationships between land use and trip generation into a single model. It is concluded that the distinction between the explanatory, analytic and predictive abilities of a regression model is artificial, and that greater emphasis on theorizing in model-construction is needed. .
Item Metadata
Title |
Multicollinearity in transportation models
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
1970
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Description |
This thesis explores some of the limitations and implications of using multiple regression analysis in transportation models. Specifically it investigates how the problem of multicollinearity, which results from using intercorrelated variables in trip generation models, adversely affects the validation of hypotheses, discovery of underlying relationships and prediction.
The research methodology consists of a review of the literature on trip generation analysis and a theoretical exposition on multicollinearity. Secondly, trip generation data for Greater Vancouver (1968) is used for empirical analysis. Factor analysis and multiple regression techniques are employed.
The results demonstrate that multicollinearity is both an explanatory and prediction problem which can be overcome by a combined factor analytic and regression method. This method is also capable of identifying and incorporating causal relationships between land use and trip generation into a single model. It is concluded that the distinction between the explanatory, analytic and predictive abilities of a regression model is artificial, and that greater emphasis on theorizing in model-construction is needed. .
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Type | |
Language |
eng
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Date Available |
2011-05-26
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0093346
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URI | |
Degree | |
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Affiliation | |
Degree Grantor |
University of British Columbia
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.