This dissertation provides three essays. In the first essay, a model with two linguistic variables is built to demonstrate the joint effect of multiple linguistic variables in a dynamic modeling context. Triangular membership function is used to represent the linguistic variables and the joint effect is captured through fuzzy inference method. In this essay, the results obtained by employing fuzzy concepts are compared with the results that one would obtain using generic lookup functions.

The second essay develops a system dynamics model by which policy makers can assess the impact of various travel demand management interventions within a metropolitan area and as a consequence understand the complex behavior of affected transportation-socioeconomic systems. This essay builds on a previously formulated approach where fuzzy concepts are used to represent five linguistic variables used in the model. We also compare the level of traffic congestion under the scenarios with and without traffic congestion pricing.

The third essay is based on the second essay where different scenarios of the travel demand management policies are evaluated and analyzed. There are two parts in this essay. The first part addresses the construction of a Management Flight Simulator (MFS) that is used to do policy analysis for travel demand management policies. By using the Management Flight Simulator, the second part of the essay describes the evaluation of alternative travel demand management policies.

In this research, we found that the revenue generated from congestion pricing does increase mass transit capacity even with the aging of mass transit capacity. However, in the short term traffic congestion is mitigated while in the long term the proposed travel demand management policy actually deteriorates the traffic situation.