Simulating adaptive, personalized, multi-modal mobility in smart cities

Andreas Poxrucker, Gernot Bahle, Paul Lukowicz

In: Smart City 360°. International Summit Smart City 360° November 22-24 Bratislava Slovakia Pages 113-124 Springer International Publishing 2016.


Smart, multi-modal transportation concepts are a key com- ponent towards smart sustainable cities. Such systems usually involve combinations of various modes of individual mobility (private cars, bicy- cles, walking), public transportation, and shared mobility (e.g. car shar- ing, car pooling). In this paper, we introduce a large-scale multi-agent simulation tool for simulating adaptive, personalized, multi-modal mobility. It is calibrated using various sources of real-world data and can be quickly adapted to new scenarios. The tool is highly modular and flexible and can be used to examine a variety of questions ranging from collec- tive adaptation over collaborative learning to emergence and emergent behaviour. We present the design concept and architecture, showcase the adaptation to a real scenario (the city of Trento, Italy) and demonstrate an example of collaborative learning.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz