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Dynamic Activity-Travel Diary Data Collection Using a Global Positioning SystemEnabled Personal Digital Assistant Bruno Kochan, Hasselt University, Transportation Research Institute Tom Bellemans, Hasselt University, Transportation Research Institute Davy Janssens, Hasselt University, Transportation Research Institute Geert Wets, Hasselt University, Transportation Research Institute Activity-based transportation models have set the stan- attempts to predict interdependencies between several dard for modeling travel demand for the last decade. It facets of activity profiles. These facets are often identi- seems common practice nowadays to collect the data to fied as which activities are conducted where, when, and estimate these activity-based transportation models by for how long, with whom, with which transport modes means of activity-travel diaries. This paper presents a being used. general functional framework of an advanced data col- As activity-based transportation models mature, they lection application for activity-travel diaries to be incorporate increasing levels of detail. An evolution deployed on a Global Positioning Systemenabled per- toward dynamic activity-based models that incorporates sonal digital assistant. The different modules, the build- learning effects can be observed in the literature (Joh ing blocks of the application, will be reviewed as well. 2004). The dynamics of travel behavior are driven by learning over time and short-term adaptation on the basis of within-day rescheduling. In contrast to static I n the past, four-step models were developed to pre- models, dynamic models try to capture these dynamics dict travel demand in the long run. The predicted through enhanced activity-travel data. To accommodate travel demand, as outcome of the four-step models, the growing data requirements for calibration and vali- can be used to support different kinds of decisions such dation of the dynamic activity-based models, more as investments in new road infrastructure. In these four- detailed activity-travel diary data must be collected. As step models, travel is assumed to be the result of four the collection of basic data for activity-travel diaries subsequent decisions that are modeled separately. More already puts a heavy burden on the respondents, new recently, especially in the 1980s and early 1990s, several techniques must be developed to allow for the collection researchers claimed that limited insight was offered into of even-more-detailed scheduling behavior data. In this the relationship between travel and nontravel aspects in paper, a general functional framework of an advanced the widely adopted four-step models. Indeed, travel has data collection application for activity-travel diaries an isolated existence in these models, and the question to be deployed on a Global Positioning System of why people undertake trips is completely neglected. (GPS)enabled personal digital assistant (PDA) is pre- This is where activity-based transportation models sented. This tool must allow for the collection of detailed come into play. The major idea behind activity-based activity-travel diary data while limiting the burden on models is that travel demand is derived from the activi- the respondents. ties that individuals and households need or wish to per- The remainder of this paper is organized as follows: form. The main difference between traditional (i.e., the next section gives an overview of the state of the art four-step) transportation forecasting methodologies and in relation to computerized collection tools for activity activity-based transportation models is that the latter diary data. Then, the advantages and disadvantages of a 94