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