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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_075.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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FEATHERS Scope This model is part of a wider research program that involves a number of other Belgian research institutes. This program aims at examining a series of issues perti- nent in the development of an activity-based model of travel demand for Flanders, Belgium. For example, new technology for collecting vehicle data will be explored as well as the application of combined GPS–personal digi- tal assistant (PDA) technology for collecting activity- travel data [PARROTS (PDA System for Activity Registration and Recording of Travel Scheduling), see Bellemans et al. 2005, Kochan et al. 2006]. Feathers (Forecasting Evolutionary Activity-Travel of Households and Their Environmental Repercussions) is the acronym given to the model, which will be based on the current status of the extended Aurora model, as explained ear- lier. However, because Aurora to date is largely based on theory only and on some numerical experiments to assess the face validity of the model, further empirical testing and operational improvement will be required. It is to be expected therefore that certain elements will be refined and that other new elements will be added. The remain- der of this section briefly addresses some of these issues. Utility Functions The core of the models is the S-shaped utility functions (Equations 2 and 3). To date, this shape, which is quite dif- ferent from that of other models of activity-time allocation, is derived from theory. No specific data to test the shape of the functions and assess its relevance have been collected to date. Therefore, one of the subprojects is concerned with collecting data on how individuals change their activity- travel patterns and testing whether the assumed S-shaped utility functions represent such change, or if not, the alter- native functional forms that are required. This project will also examine and estimate the effect of context variables that influence the maximum utility (Equation 3) that can be derived. The results will be critical in that, unlike for other models, it is assumed for this model that utility func- tions are context dependent. Finally, also to be tested is whether the assumed addition of activity and context-spe- cific utility functions to represent the overall utility of a daily activity-travel schedule can be corroborated or, if not, whether more complex forms are required. Learning It follows from the foregoing that an agent’s beliefs about the system with which he or she interacts play a role in scheduling and are updated each time a schedule is implemented. Learning may involve many mechanisms. First, it is assumed that, as explained earlier, when their activity schedules are implemented, agents will learn about the attributes or states of their environment (e.g., travel times) from experiences, which, with respect to the state of a variable, will change the subjective probabili- ties and hence the agents’ beliefs. If the actual situation is consistent with outcomes perceived as most probable, uncertainty in beliefs will be reduced, and the individuals will be more confident in predicting outcomes on future occasions. In contrast, if outcomes are contrary to expec- tations, uncertainty will increase and therefore so will difficulty of prediction and perceived value of informa- tion of future events. Second, in addition to this attribute learning, it is assumed that agents have an inherent desire to make sense of the world around them. One of the mechanisms involved is identification of the conditions that allow them to explain away differences in attributes of the environment (condition learning). For example, differences in travel times can be explained in relation to day of the week, departure time, weather conditions, an accident, and the like. The condition set is not necessar- ily constant over time but may grow or shrink. These two forms of learning imply that, only after many per- sonal experiences, agents will have gained sufficient knowledge about their environment. Reality suggests otherwise, and therefore it is assumed that agents also are capable of analogue learning and reasoning: they draw inferences about attributes of certain objects by analogy with other similar objects. Finally, in addition to these personal styles of learning, it is assumed that agents learn from being part of a social network: they learn by word of mouth from members of their social network. Similar Bayesian updating equations (see Arentze and Timmermans 2006) will be used to estimate these learn- ing processes. This is the topic of another project. Impact of Life Trajectory Events Above it has been assumed that the household context is stationary. However, in reality, the household context changes over time as a function of life trajectory events, such as a new child, another job, and the like, and this change may bring about changes in one or more facets of the activity agenda and preferences for choice alternatives. The potential relevance and impact of such events in an activity framework has been explored by van der Waerden et al. (2003a, 2003b) and has led to the formulation of a Bayesian decision network model applied to transport mode choice decisions (Verhoeven et al. 2005a, 2005b). The approach will be further evaluated and extended to multiple facets of activity-travel patterns in the context of Feathers. It constitutes another project in the program. 75MODELING SHORT-TERM DYNAMICS IN ACTIVITY-TRAVEL PATTERNS

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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