Efficient public transportation provides economic and social opportunities that increase accessibility to markets and employment as well as providing investment benefits. Key challenges to the U.S. public transportation industry include developing modes and increasing the availability of public transportation in a manner that meets the needs of individual users in a cost effective manner. A problem facing public transportation officials is the need to understand the factors that influence consumer decision-making and consumer attitudes toward public transportation. Feedback regarding experiences as well as expectations from commuters provides information for developing and improving public transportation. Thus, decision-making factors of end-users are keys to improving supply, growth, and understanding utilization of public transportation. Public transportation officials seek to improve the public transportation experience for commuters by increasing modes and benefits of the systems. The decision-making factors of the end-users require identification and examination in order to provide a high quality and efficient experience for commuters. The research questions of interest in the current dissertation are: (1) What are the decision-making factors affecting commuters’ attitudes toward public transportation? and (2) How do the end-user decision-making factors affect the supply of public transportation? The purpose of this research is to extend the current body …

This dissertation explores data fusion methodology to deduce an overall inference from the data gathered from multiple heterogeneous sources. Typically, if there existed a data source in which the data were reliable and unbiased, then data fusion would not be necessary. Data fusion methodology combines data form multiple diverse sources so that the desired information - such as the population mean - is improved despite redundancies, inaccuracies, biases, and inflated variability in the data. Examples of data fusion include estimating average demand from similar sources, and integrating fatality counts from different media sources after a catastrophe. The approach in this study combines "inputs" from distinct sources so that the information is "fused." Another way of describing this process is "data integration." Important assumptions are 1. Several sources provide "inputs" for information used to estimate parameters of a probability distribution. 2. Since distributions for the data from the sources are heterogeneous, some sources are less reliable. 3. Distortions, bias, censorship, and systematic errors may be more prominent in data from certain sources. 4. The sample size of sources data, number of "inputs," may be very small. Examples of information from multiple sources are abundant: traffic information from sensors at intersections, multiple …