By far the most popular method to account for dependencies in the transportation
network analysis literature is the use of the multivariate normal (MVN) distribution.
While in certain cases there is some theoretical underpinning for the MVN assumption, in
others there is none. This can lead to misleading results: results do not only depend on
whether dependence is modeled, but also how dependence is modeled. When assuming
the MVN distribution, one is limiting oneself to a specific set of dependency structures,
which can substantially limit validity of results. In this report an existing, more flexible,
correlation-based approach (where just marginal distributions and their correlations are
specified) is proposed, and it is demonstrated that, in simulation studies, such an
approach is a generalization of the MVN assumption. The need for such generalization is
particularly critical in the transportation network modeling literature, where oftentimes there exists no or insufficient data to estimate probability distributions, so that sensitivity
analyses assuming different dependence structures could be extremely valuable.
However, the proposed method has its own drawbacks. For example, it is again not able
to exhaust all possible dependence forms and it relies on some not-so-known properties
of the correlation coefficient. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-05-1122 |
| Date | 22 November 2010 |
| Creators | Ng, Man Wo |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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