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. . / Applied Science, Faculty of / Community and Regional Planning (SCARP), School of / Graduate
| Identifer | oai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/34904 |
| Date | January 1970 |
| Creators | Chan , Sheung-Ling |
| Publisher | University of British Columbia |
| Source Sets | University of British Columbia |
| Language | English |
| Detected Language | English |
| Type | Text, Thesis/Dissertation |
| 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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