Suggesting just enough (un)crowded routes and destinations

Though people like to visit popular places, for health-related concerns and due to the recent restrictions adopted around the world, gatherings should be avoided. When planning a trip, one has to consider both attractiveness in terms of general interest for the destinations, and the density of people gathering there. In this work, we propose a recommendation system aiming at offering users some suggestions on useful routes and destinations that balance both liveliness and overcrowding. Firstly, we use datasets storing GPS positions as a basis for the statistics on routes and destinations. Then, we use an accurate probability algorithm that estimates the number of people moving from one place to another in the city and accordingly we show a list of destinations to users. The destination points are filtered based on the user’s preference on the density of people. A multi-agent system is used to handle the user requests to find a route for a trip, statistics on possible destinations, and suggestions to users. Thanks to our solution we can inform users on suitable routes and destinations, as well as alert them when a preferred destination is overcrowded.