Starting Point

Both domestic and foreign visitors are often on the move in Switzerland by public transport or car and – without knowing it – drive past sights and excursion opportunities. They actually have time to make a detour or would like to remember this sight for a later visit. IN general: There are plenty of experiences in Switzerland that people don’t know, even if they have passed them several times without knowing it. We want to give our guests (and of course Swiss inhabitants) the possibility to explore experiences nearby, which can be accessed within a certain amount of time.

Goal / Task

Realisation of a system (e.g. app) that draws the guest’s attention to sights in the current surroundings. The type of attention that is helpful here is to be worked out. There are a few sub-goals to achive:

• Visualize the experiences on a map which are reachable within 1 hour
• Giving the user the possibility to define if they’re travelling by car or by public transport
• Create a prediction for a visitor, based on it’s moving behaviour

Data

The largest collection of tourist content in Switzerland is obtained via the Open Data API of Switzerland Tourism at https://www.myswitzerland.io . The challenge was focussing on experiences and therfore useing the 'attractions' endpoint.

Result

The main outcome is an app concept, which is adressing the travelling distance to the experiences nearby. It's giving the users the possibility to inactivate the recommendation system, bringing the app into a 'do not disturb' mode. Instead of filtering experiences, based on the travelling distance, the concept is adressing this issue by just giving the user the information, how much time you need to travel to the experience. This is much better than just filtering the experiences (e.g. what if an interesting experience is 1h 5 minutes away from the current location, and the filter is defined to 1h? -> Instead of technically filtering those experiences, we're giving the users all kind of information and let them filter by themself.

The concept is technically validated with Node-Red (https://www.nodered.org). Please find the presentation here (Link to YouTube):

• Original Figma
• PDF Export Figma
• Node-Red Configuration File

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