Authors:
Patrik Gonçalves
1
and
Harald Baier
2
Affiliations:
1
Zentrale Stelle für Informationstechnik im Sicherheitsbereich, Zamdorfer Straße 88, München, Germany
;
2
Research Institute Cyber Defence (CODE), Universität der Bundeswehr München, Carl-Wery-Straße 22, München, Germany
Keyword(s):
Activity-Based Models, Non-Typical Behavior, Preset, Custom, A-Priori, Activities, Key Events, Outliers, Human Mobility, Dataset, Intra-Day.
Abstract:
The generation of synthetic human mobility scenarios is often realized through data-driven or rule-based approaches. They work in a fire-and-forget principle and provide limited support to induce controlled activities in simulated scenarios. However, including controlled preset activities in the generation phase enables the creation of mobility scenarios that include a-priori known outliers or key events. Such mobility test datasets might be used in outlier detection for machine learning algorithms or for inducing non-typical mobility, where models do not exist or are too complex to construct. In this work we propose an activity-based scheduler to include controlled preset key events in the scheduling process of daily human mobility scenarios. Further, with our rule-based approach we can synthesize new activities of a target region even when initial data is unavailable or missing. In addition we propose a hierarchical methodology to iteratively add activities according to their numbe
r of constraints and provide a publicly available Python-based implementation. Our validation shows that our approach is able to integrate non-typical behavior in typical mobility scenarios.
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