Festivals Influence Mobility Similar to Floods

This applies both locally and partially on a national level. The findings could help to better understand the complex impacts of natural hazards, reports a Swiss-Austrian research team in a recent publication in the journal "npj Complexity".

Specifically, the traffic flows on roads and railways around an extreme flood in Zofingen, Switzerland, in 2017 were examined over a period of six weeks. From this, one could derive the usual mobility behavior, i.e., how many people move when and how in the 350 square kilometer region around Zofingen, as well as the deviations during the floods. The study, based on location data from mobile phones, showed further similar distortions in the weeks before and after the flood.

"This surprised us because we knew there was only one flood," explained Margreth Keiler, who works at the Institute for Interdisciplinary Mountain Research of the Austrian Academy of Sciences (ÖAW) and at the Institute of Geography at the University of Innsbruck, in an interview with APA. It quickly became clear that there was a connection between these mobility changes and major events such as concerts, markets, and summer festivals.

Detours due to closures and traffic jams

According to the researcher, the similarities arise, among other things, because in both cases one has to take detours: both due to roads and railways affected by flooding and therefore closed, and due to planned and traffic-technically necessary detours at larger festivals. Mobility behavior is also influenced and, in this case, slowed down by the resulting traffic jams on the limited connections. On a national level, the differences in the distribution of visitors and the distances traveled were smaller, also depending on whether it was a supra-regional festival, to which people from all over Switzerland travel, or regional events.

Since major social events occur significantly more often than floods and thus more data is available, the analysis could help in building models for disaster management and other traffic and urban planning tasks, according to study leader Simone Loreti from the University of Bern. The combination of machine learning, complexity research, and network science offers great potential in any case.

(APA/Red)

This article has been automatically translated, read the original article here.