Author

Andreas Poxrucker, Gernot Bahle, Paul Lukowicz

Abstract

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.

BibTex

@inproceedings {Poxrucker:Simulating:2016:8896,
	number = {}, 
	month = {}, 
	year = {2016}, 
	title = {Simulating adaptive, personalized, multi-modal mobility in smart cities}, 
	journal = {}, 
	volume = {}, 
	pages = {113-124}, 
	publisher = {Springer International Publishing}, 
	author = {Andreas Poxrucker, Gernot Bahle, Paul Lukowicz}, 
	keywords = {multi-agent simulations, smart urban mobility, socio-technical systems, collective adaptive systems, collaborative learning}
}