This research develops a combined qualitative and quantitative activity based analysis methodologies framework for integrated fixed activity chaining schedules (IFACS). What is central to my dissertation is the extent to which the type, sequence, duration, and timing of fixed activities influence trip chaining. The goal is to improve the practice of travel behavior forecasting by modeling the activity patterns and chaining behavior of individuals with restricted schedules. Cluster analysis methods are applied to narrow down the endless possibilities of individual trip decision-making into a manageable group. Then space-time prism concepts are applied to locate unique activity travel patterns. Qualitative examples show that aggregate and individual trip-chaining behaviors are not easy to model. Informed by the qualitative analysis results, the framework incorporates both discrete choice and time-to-event models as quantitative analysis tools. The quantitative models cover two levels of chaining: trip link and whole journey. Logit models are used to analyze the choice to make a chained link right after the completion of an activity, and the decision to conduct a chained journey. Poisson models are used to examine the decision to make a number of chained links on a journey. Hazard-based duration models are used to analyze time of chaining events by studying the duration of a chained journey, then the duration of time elapsed till a chained link is made right after a fixed activity with the goal of defining a time threshold to the occurrence of trip chaining. Integrating results from qualitative and quantitative methods leads to a better understanding of how people make their trip-chaining and travel behavior decisions. Data from the 1998 Mobidrive six-week travel diary survey is used in the analyses. This framework focuses on the effects of five types of fixed activities on trip-chaining behavior: work, work-related, school, further education, and club/group meetings. Understanding factors influencing a person's decision to participate in a trip chain allows motivations behind trip-chaining behavior to become more tangible. One of the highlights of the analysis results of the modeling framework is discovering that the chaining behavior of individuals is influenced in different ways among different population subgroups.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:dissertations-4248 |
| Date | 01 January 2006 |
| Creators | Al-Jammal, Rana |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Language | English |
| Detected Language | English |
| Type | text |
| Source | Doctoral Dissertations Available from Proquest |
Page generated in 0.0139 seconds