Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 202-205. / by Kamal Nabil Ali Safwat. / Ph.D.

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This study sought to identify the key determinants that lead individuals to participate in the transportation planning process. Two models of participation, one for the short-term and another for the long-term, were developed to test whether the key internal and external determinants are responsible for influencing a person's willingness to participate. The data for this study came from a mail-back survey that was administered to a random sample of 570 individuals throughout the State of Florida for a response rate of 37.37 percent. The results indicate that the internal motivational determinants exert more influence on a person's short-term willingness to participate as compared to a person's long-term willingness to participate. In addition, the external social capital determinants exert more influence on a person's long-term willingness to participate as compared to a person's short-term willingness to participate. However, only one of the three external citizenship orientation variables, participatory citizenship orientation, was found to be influential in determining a person's short-term willingness to participate. Recommendations were made for public managers to work collaboratively with the public as a participative facilitator, thereby opening up the decision-making process to the general public. One suggested course of action is for public managers to use existing civic organizations as a base for widening an agency's long-term planning outreach programs. In addition, suggestions for future research propose that qualitative studies delve in-depth into the positive/negative feelings related to participation, as well as look at how different public participation techniques may affect a person's willingness to participate, especially as it relates to different time frames.

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The inherent uncertainty in travel forecasting models -- arising from errors in input data, parameter estimation, or model formulation -- is receiving increasing attention from the scholarly and practicing community. In this research, we investigate the variance in forecasted traffic volumes resulting from varying the mode and destination choice parameters in an advanced trip-based travel demand model. Using Latin hypercube sampling to construct several hundred combinations of parameters across the plausible parameter space, we introduce substantial changes to mode and destination choice logsums and probabilities. However, the aggregate effects of of these changes on forecasted traffic volumes is small, with a variance of approximately 1 percent on high-volume facilities. Thus, parameter uncertainty does not appear to be a significant factor in forecasting traffic volumes using transportation demand models.

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