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The development of activity-based models of transport demand constitutes one of the most rapidly growing fields of interest in transportation research. Originating from the earlier human activity analysis framework developed in the early 1970's, activity-based models explicitly recognize the derived nature of travel as the need to participate in spatially disperse activities. Much of the popularity of these models lie in their ability to model and predict more specific and detailed travel demand management measures, such as longer opening hours of businesses, car pool and road pricing policies, etc. Traditional four-step transportation planning models with their focus on origin-destination pairs and the single-mode trip between these points, do not adequately address these non-infrastructural policy measures. Another major weakness of the traditional approach is the loss of the context of the trip as being within a daily activity program. In supporting these more policy sensitive measures and satisfying the increasing demand for decision support tools, many different modeling approaches have recently been advocated, including constraints-based models such as MASTIC, utility-based models such as the Daily Scheduling Model, PCATS-RUM, and PETRA, linear-programming models, and computational process models such as Scheduler to name a few. These models have in common at least two drawbacks. First, none of the models addresses multi-modal transportation systems in a considerable amount of detail. Transport modes are usually linked to trips; the possibility of more than one transport mode during a trip is typically excluded. This is a shortcoming as multi-modal transportation systems are viewed as a potential solution to current urban transportation problems. Little is known however, about the impact of multi-modal systems on activity schedules and related travel patterns. At the very least, existing activity-based models need to be adjusted to incorporate transport mode chains embedded in multi-modal systems. Secondly, current activity-based models tend to focus on the explanation and prediction of observed, cross-sectional activity-travel patterns. Modeling the dynamics of activity patterns is still scarce, although some teams have announced plans into this direction. These plans tend to focus on the short-term dynamics, that is, the problem of (re)scheduling activities as a function of network congestion or unexpected events. Attempts of modeling mid-term or long-term dynamics are virtually lacking, at least in the activity-based tradition. To fill this gap, a consortium of universities participating in the Netherlands Graduate School of Urban Research (NETHUR) decided to join forces in a collaborative effort to develop a comprehensive and integral modeling system that allows one to assess the long-term, mid-term and short-term dynamics of multi-modal transportation systems on activity and destination choice. The research program, called Amadeus, is funded by the Dutch Organization of Scientific Research (NWO) for a period of 4 years
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