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Trip chaining and mode choice are two important travel behavior decisions in activity-based travel demand modeling system. The hierarchy of these two decisions influences models predictive capability and policy sensitivity. This thesis is aimed at investigating the hierarchical relationship between these decisions and also the effects of socio-demographic characteristics on them. Structural Equation Modeling (SEM) technique is used for this investigation. A six week travel diary data collected in Thurgau, Switzerland in 2003 is used for model estimation. Model estimation results show that for work-tour, trip chain and mode choice decisions are simultaneous and it remains consistent across the six weeks. For weekdays non-work tour, mode choice precedes trip chain whereas for weekends non-work tour trip chain precedes mode choice. The investigation of the effect of a number of socio-demographic characteristics on trip chaining and mode choice behaviors is also found useful for better understanding of these behaviors. / Transportation Engineering

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Work trips are considered one of public transportation's biggest markets. It is important to understand this market as fully as possible because of the potential for congestion mitigation or air quality improvements, among other things. While much effort has been invested in researching travel on public transit, very little effort has been concentrated in understanding the work travel market. This document attempts to be a comprehensive resource which can provide current information on the persons and trips being taken on public transit for travel to/from work. This research looks at the 2009 National Household Travel Survey (NHTS) to examine the difference between work and non-work transit trips and riders, choice and captive work trips and riders, and New York City (NYC) and non-NYC work trips and riders. In addition, transit mode share for work trips across an array of socio-demographic factors is examined. As a supplement, historical trend data utilizing past NHTS/Nationwide Personal Transportation Surveys (NPTS), the American Community Survey, and American Public Transportation Association data is used. The data analysis is primarily descriptive in nature and is performed in SPSS (a statistical analysis software) and then graphed in Microsoft Excel. Interpretation of the results is based upon a comprehensive literature review and the author's knowledge and familiarity in the field of public transit. There were several interesting and noteworthy findings uncovered during this research. In the historical trend analysis, it was discovered that the proportion of transit trips being for travel to/from work has declined since 1983. Further analysis appears to indicate that the decline is being caused by new riders who are making new non-work trips. This comes as the existing work riders and trip numbers appear to be holding steady. More research is needed to confirm this finding and to probe for more specific reasons on why this is occurring. In terms of work transit trips, it was discovered that they tend to be taken on faster modes (such as commuter rail and elevated/subway) resulting in slightly higher in-vehicle speeds than non-work transit trips (11.8 MPH versus 11.2 MPH). The analysis also showed that those commuting via non-transit modes were 20 percent more likely to make a stop on their commute than those that took transit. Of those commuting on transit, riders were more likely to make a stop on their way home from work rather than to work. An analysis of choice and captive riders found that in the 16-24 year old age group, 85 percent of riders were choice riders. This finding could perhaps signify that more young adults are choosing to ride transit. However, this result should be interpreted with caution because of the small sample size and potential data issues with the 2009 NHTS dataset. More research should be carried out in the future to verify these results.

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