“Activity space,” defined as the local areas within which people move or travel during the course of their activities during a specified time period, is a measure of an individual's spatial behavior that captures individual and environmental differences and offers an alternative approach to studying the spatial reach of travelers. The shape and the area of activity space are a product of how it is conceptualized and measured. This paper enlarges the set of geometries that can be used to describe activity space. It tests four parametric geometries (ellipse, superellipse, Cassini oval, and bean curve), which are identified as those capturing a specific share of all locations visited (i.e., 95%) while minimizing the area covered. They are estimated for a number of long-duration data sets while distinguishing among trip purposes. This paper presents a flexible, easily adaptable method for calculating activity spaces of different shapes and a qualitative comparison of the four shape types on the basis of the given surveys. The choice of an appropriate shape representing an individual's activity space is highly dependent on the spatial distributions and frequencies of the locations visited by the person in the given time period.

The typical method to couple activity-based demand generation (ABDG) and dynamic traffic assignment (DTA) is time-dependent origin-destination (O-D) matrices. With that coupling method, the individual traveler's information gets lost. Delays at one trip do not affect later trips. However, it is possible to retain the full agent information from the ABDG by writing out all agents' plans, instead of the O-D matrix. A plan is a sequence of activities, connected by trips. Because that information typically is already available inside the ABDG, this is fairly easy to achieve. Multiagent simulation (MATSim) takes such plans as input. It iterates between the traffic flow simulation (sometimes called network loading) and the behavioral modules. The currently implemented behavioral modules are route finding and time adjustment. Activity resequencing or activity dropping are conceptually clear but not yet implemented. Such a system will react to a time-dependent toll by possibly rearranging the complete day; in consequence, it goes far beyond DTA (which just does route adaptation). This paper reports on the status of the current Berlin implementation. The initial plans are taken from an ABDG, originally developed by Kutter; to the authors' knowledge, this is the first time traveler-based information (and not just O-D matrices) is taken from an ABDG and used in a MATSim. The simulation results are compared with real-world traffic counts from about 100 measurement stations.

The "Last-Mile Evacuation" research project develops a numerical last mile tsunami early warning and evacuation information system on the basis of detailed earth observation data and techniques as well as unsteady, hydraulic numerical modeling of small-scale flooding and inundation dynamics of the tsunami including evacuation simulations in the urban coastal hinterland for the city of Padang, West Sumatra, Indonesia. It is well documented that Sumatra's third largest city with almost one million inhabitants is located directly on the coast and partially sited beneath the sea level, and thus, is located in a zone of extreme risk due to severe earthquakes and potential triggered tsunamis. "Last-Mile" takes the inundation dynamics into account and additionally assesses the physical-technical susceptibility and the socioeconomic vulnerability of the population with the objective to mitigate human and material losses due to possible tsunamis. By means of discrete multi-agent techniques risk-based, time-and site-dependent forecasts of the evacuation behavior of the population and the flow of traffic in large parts of the road system in the urban coastal strip are simulated and concurrently linked with the other components.

Summary. The evacuation of whole cities or even regions is an important problem, as demonstrated by recent events such as evacuation of Houston in the case of Hurricane Rita or the evacuation of coastal cities in the case of Tsunamis. A robust and flexible simulation framework for such large-scale disasters helps to predict the evacuation process. Existing methods are either geared towards smaller problems (e.g. Cellular Automata techniques or methods based on differential equations) or are not microscopic (e.g. methods based on dynamic traffic assignment). This paper presents a technique that is both microscopic and capable to process large problems.